The problem of robust extraction of visual odometry from a sequence of images obtained by an
eye in hand camera configuration is addressed. A novel approach toward solving planar
template based tracking is proposed which performs a non-linear image alignment for
successful retrieval of camera transformations. In order to obtain global optimum a biometaheuristic
is used for optimization of similarity among the planar regions. The proposed
method is validated on image sequences with real as well as synthetic transformations and
found to be resilient to intensity variations. A comparative analysis of the various similarity
measures as well as various state-of-art methods reveal that the algorithm succeeds in tracking
the planar regions robustly and has good potential to be used in real applications.
Visual tracking using particle swarm optimizationcsandit
The problem of robust extraction of visual odometry from a sequence of images obtained by an
eye in hand camera configuration is addressed. A novel approach toward solving planar
template based tracking is proposed which performs a non-linear image alignment for
successful retrieval of camera transformations. In order to obtain global optimum a biometaheuristic
is used for optimization of similarity among the planar regions. The proposed
method is validated on image sequences with real as well as synthetic transformations and
found to be resilient to intensity variations. A comparative analysis of the various similarity
measures as well as various state-of-art methods reveal that the algorithm succeeds in tracking
the planar regions robustly and has good potential to be used in real applications.
A MORPHOLOGICAL MULTIPHASE ACTIVE CONTOUR FOR VASCULAR SEGMENTATIONijbbjournal
This paper presents a morphological active contour ideal for vascular segmentation in biomedical images.
The unenhanced images of vessels and background are successfully segmented using a two-step
morphological active contour based upon Chan and Vese’s Active Contour without Edges. Using dilation
and erosion as an approximation of curve evolution, the contour provides an efficient, simple, and robust
alternative to solving partial differential equations used by traditional level-set Active Contour models. The
proposed method is demonstrated with segmented data set images and compared to results garnered from
multiphase Active Contour without Edges, morphological watershed, and Fuzzy C-means segmentations.
This paper proposes a novel technique for detecting point landmarks in 3D medical images based on phase congruency (PC). A bank of 3D log-Gabor filters is used to compute energy maps from the images. These energy maps are combined to form the PC measure, which is invariant to intensity variations and provides good feature localization. Significant 3D point landmarks are detected by analyzing the eigenvectors of PC moments computed at each point. The method is demonstrated on head and neck images for radiation therapy planning.
Exploration of Normalized Cross Correlation to Track the Object through Vario...iosrjce
Object tracking is a process devoted to locate the pathway of moving object in the succession of
frames. The tracking of the object has been emerged as a challenging facet in the fields of robot navigation,
military, traffic monitoring and video surveillance etc. In the first phase of contributions, the tracking of object
is exercised by means of matching between the template and exhaustive image through the Normalized Cross
Correlation (NCCR). In order to update the template, the moving objects are detected using frame difference
technique at regular interval of frames. Subsequently, NCCR or Principal Component Analysis (PCA) or
Histogram Regression Line (HRL) of the template and moving objects are estimated to find the best match to
update the template. The second phase discusses the tracking of object between the template and partitioned
image through the NCCR with reduced computational aspects. However, the updating schemes remain same.
Here, an exploration with varied bench mark dataset has been carried out. Further, the comparative analysis of
the proposed systems with different updating schemes such as NCCR, PCA and HRL has been succeeded. The
offered systems considerably reveal the capability to track an object indisputably under diverse illumination conditions.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document provides an overview of medical image segmentation techniques. It discusses fundamental medical image processing steps such as image capture, enhancement, segmentation, and feature extraction. Various segmentation methods are reviewed, including thresholding, region-based, edge detection, and hybrid techniques. The document emphasizes the need for developing robust segmentation methods that can recognize malignant growths early to improve cancer treatment outcomes.
Leader follower formation control of ground vehicles using camshift based gui...ijma
Autonomous ground vehicles have been designed for the purpose of that relies on ranging and bearing
information received from forward looking camera on the Formation control . A visual guidance control
algorithm is designed where real time image processing is used to provide feedback signals. The vision
subsystem and control subsystem work in parallel to accomplish formation control. A proportional
navigation and line of sight guidance laws are used to estimate the range and bearing information from the
leader vehicle using the vision subsystem. The algorithms for vision detection and localization used here
are similar to approaches for many computer vision tasks such as face tracking and detection that are
based color-and texture based features, and non-parametric Continuously Adaptive Mean-shift algorithms
to keep track of the leader. This is being proposed for the first time in the leader follower framework. The
algorithms are simple but effective for real time and provide an alternate approach to traditional based
approaches like the Viola Jones algorithm. Further to stabilize the follower to the leader trajectory, the
sliding mode controller is used to dynamically track the leader. The performance of the results is
demonstrated in simulation and in practical experiments.
Image fusion is a technique of
intertwining at least two pictures of same scene to
shape single melded picture which shows indispensable
data in the melded picture. Picture combination
system is utilized for expelling clamor from the
pictures. Commotion is an undesirable material which
crumbles the nature of a picture influencing the
lucidity of a picture. Clamor can be of different kinds,
for example, Gaussian commotion, motivation clamor,
uniform commotion and so forth. Pictures degenerate
some of the time amid securing or transmission or
because of blame memory areas in the equipment.
Picture combination should be possible at three
dimensions, for example, pixel level combination,
highlight level combination and choice dimension
combination. There are essentially two kinds of picture
combination methods which are spatial area
combination systems and transient space combination
procedures. (PCA) combination, Normal strategy, high
pass sifting are spatial area techniques and strategies
which incorporate change, for example, Discrete
Cosine Transform, Discrete wavelet change are
transient space combination strategies. There are
different techniques for picture combination which
have numerous favorable circumstances and
detriments. Numerous procedures experience the ill
effects of the issue of shading curios that comes in the
intertwined picture shaped. Also, the Cyclopean One
of the most astonishing properties of human stereo
vision is the combination of the left and right
perspectives of a scene into a solitary cyclopean one.
Under typical survey conditions, the world shows up as
observed from a virtual eye set halfway between the
left and right eye positions. The apparent picture of
the world is never recorded specifically by any tangible
exhibit, however developed by our neural equipment.
The term cyclopean alludes to a type of visual
upgrades that is characterized by binocular
dissimilarity alone. He suspected that stereo-psis may
find concealed articles, this may be helpful to discover
disguised items. The critical part of this examination
when utilizing arbitrary dab stereo-grams was that
uniqueness is adequate for stereo-psis, and where had
just demonstrated that binocular difference was vital
for stereo-psis.
Covariance models for geodetic applications of collocation brief versionCarlo Iapige De Gaetani
This document summarizes a new methodology for modeling covariance functions for integrated gravity field modeling using collocation. The methodology uses linear programming and the simplex method to estimate parameters of analytical covariance function models to best fit empirical covariance functions from multiple gravity observations. This allows all available empirical covariances to be considered simultaneously, improving over standard methods that model covariances separately and propagate between observations. The results from testing this new methodology show improvements over existing software packages for modeling covariance functions for local gravity applications.
Visual tracking using particle swarm optimizationcsandit
The problem of robust extraction of visual odometry from a sequence of images obtained by an
eye in hand camera configuration is addressed. A novel approach toward solving planar
template based tracking is proposed which performs a non-linear image alignment for
successful retrieval of camera transformations. In order to obtain global optimum a biometaheuristic
is used for optimization of similarity among the planar regions. The proposed
method is validated on image sequences with real as well as synthetic transformations and
found to be resilient to intensity variations. A comparative analysis of the various similarity
measures as well as various state-of-art methods reveal that the algorithm succeeds in tracking
the planar regions robustly and has good potential to be used in real applications.
A MORPHOLOGICAL MULTIPHASE ACTIVE CONTOUR FOR VASCULAR SEGMENTATIONijbbjournal
This paper presents a morphological active contour ideal for vascular segmentation in biomedical images.
The unenhanced images of vessels and background are successfully segmented using a two-step
morphological active contour based upon Chan and Vese’s Active Contour without Edges. Using dilation
and erosion as an approximation of curve evolution, the contour provides an efficient, simple, and robust
alternative to solving partial differential equations used by traditional level-set Active Contour models. The
proposed method is demonstrated with segmented data set images and compared to results garnered from
multiphase Active Contour without Edges, morphological watershed, and Fuzzy C-means segmentations.
This paper proposes a novel technique for detecting point landmarks in 3D medical images based on phase congruency (PC). A bank of 3D log-Gabor filters is used to compute energy maps from the images. These energy maps are combined to form the PC measure, which is invariant to intensity variations and provides good feature localization. Significant 3D point landmarks are detected by analyzing the eigenvectors of PC moments computed at each point. The method is demonstrated on head and neck images for radiation therapy planning.
Exploration of Normalized Cross Correlation to Track the Object through Vario...iosrjce
Object tracking is a process devoted to locate the pathway of moving object in the succession of
frames. The tracking of the object has been emerged as a challenging facet in the fields of robot navigation,
military, traffic monitoring and video surveillance etc. In the first phase of contributions, the tracking of object
is exercised by means of matching between the template and exhaustive image through the Normalized Cross
Correlation (NCCR). In order to update the template, the moving objects are detected using frame difference
technique at regular interval of frames. Subsequently, NCCR or Principal Component Analysis (PCA) or
Histogram Regression Line (HRL) of the template and moving objects are estimated to find the best match to
update the template. The second phase discusses the tracking of object between the template and partitioned
image through the NCCR with reduced computational aspects. However, the updating schemes remain same.
Here, an exploration with varied bench mark dataset has been carried out. Further, the comparative analysis of
the proposed systems with different updating schemes such as NCCR, PCA and HRL has been succeeded. The
offered systems considerably reveal the capability to track an object indisputably under diverse illumination conditions.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document provides an overview of medical image segmentation techniques. It discusses fundamental medical image processing steps such as image capture, enhancement, segmentation, and feature extraction. Various segmentation methods are reviewed, including thresholding, region-based, edge detection, and hybrid techniques. The document emphasizes the need for developing robust segmentation methods that can recognize malignant growths early to improve cancer treatment outcomes.
Leader follower formation control of ground vehicles using camshift based gui...ijma
Autonomous ground vehicles have been designed for the purpose of that relies on ranging and bearing
information received from forward looking camera on the Formation control . A visual guidance control
algorithm is designed where real time image processing is used to provide feedback signals. The vision
subsystem and control subsystem work in parallel to accomplish formation control. A proportional
navigation and line of sight guidance laws are used to estimate the range and bearing information from the
leader vehicle using the vision subsystem. The algorithms for vision detection and localization used here
are similar to approaches for many computer vision tasks such as face tracking and detection that are
based color-and texture based features, and non-parametric Continuously Adaptive Mean-shift algorithms
to keep track of the leader. This is being proposed for the first time in the leader follower framework. The
algorithms are simple but effective for real time and provide an alternate approach to traditional based
approaches like the Viola Jones algorithm. Further to stabilize the follower to the leader trajectory, the
sliding mode controller is used to dynamically track the leader. The performance of the results is
demonstrated in simulation and in practical experiments.
Image fusion is a technique of
intertwining at least two pictures of same scene to
shape single melded picture which shows indispensable
data in the melded picture. Picture combination
system is utilized for expelling clamor from the
pictures. Commotion is an undesirable material which
crumbles the nature of a picture influencing the
lucidity of a picture. Clamor can be of different kinds,
for example, Gaussian commotion, motivation clamor,
uniform commotion and so forth. Pictures degenerate
some of the time amid securing or transmission or
because of blame memory areas in the equipment.
Picture combination should be possible at three
dimensions, for example, pixel level combination,
highlight level combination and choice dimension
combination. There are essentially two kinds of picture
combination methods which are spatial area
combination systems and transient space combination
procedures. (PCA) combination, Normal strategy, high
pass sifting are spatial area techniques and strategies
which incorporate change, for example, Discrete
Cosine Transform, Discrete wavelet change are
transient space combination strategies. There are
different techniques for picture combination which
have numerous favorable circumstances and
detriments. Numerous procedures experience the ill
effects of the issue of shading curios that comes in the
intertwined picture shaped. Also, the Cyclopean One
of the most astonishing properties of human stereo
vision is the combination of the left and right
perspectives of a scene into a solitary cyclopean one.
Under typical survey conditions, the world shows up as
observed from a virtual eye set halfway between the
left and right eye positions. The apparent picture of
the world is never recorded specifically by any tangible
exhibit, however developed by our neural equipment.
The term cyclopean alludes to a type of visual
upgrades that is characterized by binocular
dissimilarity alone. He suspected that stereo-psis may
find concealed articles, this may be helpful to discover
disguised items. The critical part of this examination
when utilizing arbitrary dab stereo-grams was that
uniqueness is adequate for stereo-psis, and where had
just demonstrated that binocular difference was vital
for stereo-psis.
Covariance models for geodetic applications of collocation brief versionCarlo Iapige De Gaetani
This document summarizes a new methodology for modeling covariance functions for integrated gravity field modeling using collocation. The methodology uses linear programming and the simplex method to estimate parameters of analytical covariance function models to best fit empirical covariance functions from multiple gravity observations. This allows all available empirical covariances to be considered simultaneously, improving over standard methods that model covariances separately and propagate between observations. The results from testing this new methodology show improvements over existing software packages for modeling covariance functions for local gravity applications.
International Journal of Engineering Inventions (IJEI) provides a multidisciplinary passage for researchers, managers, professionals, practitioners and students around the globe to publish high quality, peer-reviewed articles on all theoretical and empirical aspects of Engineering and Science.
Robust Clustering of Eye Movement Recordings for QuantiGiuseppe Fineschi
Characterizing the location and extent of a viewer’s interest, in terms of eye movement recordings, informs a range of investigations in image and scene viewing. We present an automatic data-driven method for accomplishing this, which clusters visual point-of-regard (POR) measurements into gazes and regions-ofinterest using the mean shift procedure. Clusters produced using this method form a structured representation of viewer interest, and at the same time are replicable and not heavily influenced by noise or outliers. Thus, they are useful in answering fine-grained questions about where and how a viewer examined an image.
EVALUATION OF THE VISUAL ODOMETRY METHODS FOR SEMI-DENSE REAL-TIMEacijjournal
This document summarizes the evaluation of visual odometry methods for semi-dense real-time reconstruction. It discusses two popular visual odometry approaches, LSD-SLAM and ORB-SLAM2, and evaluates their performance on three datasets. It then proposes a new approach that combines feature-based and feature-less methods for real-time odometry with a stereo camera, matching images semi-densely and reconstructing 3D environments directly on pixels with gradients.
A study of a modified histogram based fast enhancement algorithm (mhbfe)sipij
Image enhancement is one of the most important issues in low-level image processing. The goal of image
enhancement is to improve the quality of an image such that enhanced image is better than the original
image. Conventional Histogram equalization (HE) is one of the most algorithms used in the contrast
enhancement of medical images, this due to its simplicity and effectiveness. However, it causes the
unnatural look and visual artefacts, where it tends to change the brightness of an images. The Histogram
Based Fast Enhancement Algorithm (HBFE) tries to enhance the CT head images, where it improves the
water-washed effect caused by conventional histogram equalization algorithms with less complexity. It
depends on using full gray levels to enhance the soft tissues ignoring other image details. We present a
modification of this algorithm to be valid for most CT image types with keeping the degree of simplicity.
Experimental results show that The Modified Histogram Based Fast Enhancement Algorithm (MHBFE)
enhances the results in term of PSNR, AMBE and entropy. We use also the Statistical analysis to ensure
the improvement of the proposed modification that can be generalized. ANalysis Of VAriance (ANOVA) is
used as first to test whether or not all the results have the same average. Then we find the significant
improvement of the modification.
Performance analysis of contourlet based hyperspectral image fusion methodijitjournal
Recently, contourlet transform has been widely used in hyperspectral image fusion due to its advantages,
such as high directionality and anisotropy; and studies show that the contourlet-based fusion methods
perform better than the existing conventional methods including wavelet-based fusion methods. Few studies
have been done to comparatively analyze the performance of contourlet-based fusion methods;
furthermore, no research has been done to analyze the contourlet-based fusion methods by focusing on
their unique transform mechanisms. In addition, no research has focused on the original contourlet
transform and its upgraded versions. In this paper, we investigate three different kinds of contourlet
transform: i) original contourlet transform, ii) nonsubsampled contourlet transform, iii) contourlet
transform with sharp frequency localization. The latter two transforms were developed to overcome the
major drawbacks of the original contourlet transform; so it is necessary and beneficial to see how they
perform in the context of hyperspectral image fusion. The results of our comparative analysis show that the
latter two transforms perform better than the original contourlet transform in terms of increasing spatial
resolution and preserving spectral information.
An efficient image segmentation approach through enhanced watershed algorithmAlexander Decker
This document proposes an efficient image segmentation approach combining an enhanced watershed algorithm and color histogram analysis. The watershed algorithm is applied to preprocessed images after merging the results with an enhanced edge detection. Over-segmentation issues are addressed through a post-processing step applying color histogram analysis to each segmented region, improving overall performance. The document provides background on image segmentation techniques, reviews related work applying watershed algorithms, and discusses challenges like over-segmentation that watershed approaches can face.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Novel Framework for Navigation using Enhanced Fuzzy Approach with Sliding Mod...IJECEIAES
The reliability of any embedded navigator in advanced vehicular system depends upon correct and precise information of navigational data captured and processed to offer trustworthy path. After reviewing the existing system, a significant trade-off is explored between the existing navigational system and present state of controller design on various case studies and applications. The existing design of controller system for navigation using error-prone GPS/INS data doesn’t emphasize on sliding mode controller. Although, there has been good number of studies in sliding mode controller, it is less attempted to optimize the navigational performance of a vehicle. Therefore, this paper presents a novel optimized design of a sliding mode controller that can be effectively deployed on advanced navigational system. The study outcome was found to offer higher speed, optimal control signal, and lower error occurances to prove that proposed system offers reliable and optimized navigational services in contrast to existing system.
An Efficient Algorithm for the Segmentation of Astronomical ImagesIOSR Journals
This document proposes an efficient algorithm for segmenting celestial objects from astronomical images. The algorithm uses multiple preprocessing steps including removing bright point sources, stationary wavelet transform, total variation denoising, and adaptive histogram equalization. Level set segmentation is then used as the key technique for segmentation. Preprocessing helps overcome issues like noise, weak object edges, and low contrast. Level set segmentation can segment objects while retaining their texture and shape information for subsequent classification. The algorithm is tested on various celestial objects and shown to effectively segment them.
In the past decade, seismic tomography techniques have improved through more sophisticated methodologies and technologies. An important issue is the accuracy and density of residual move-out (RMO) picks used to derive tomographic velocity model updates. A new automated method allows for precise tracking of RMO on pre-stack depth-migrated gathers, enabling fast determination of dense, high-quality travel time residuals for seismic tomography. This leads to better resolution of small-scale anomalies and flatter pre-stack depth-migrated gathers, providing better-focused structural images.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
Chest radiograph image enhancement with wavelet decomposition and morphologic...TELKOMNIKA JOURNAL
Medical image processing algorithms significantly affect the precision of disease diagnostic
process. This makes it crucial to improve the quality of a medical image with the goal to enhance
perceivability of the points of interest in order to obtain accurate diagnosis of a patient. Despite the
reliance of various medical diagnostics on X-rays, they are usually plagued by dark and low contrast
properties. Sought-after details in X-rays can only be accessed by means of digital image processing
techniques, despite the fact that these techniques are far from being perfect. In this paper, we implement
a wavelet decomposition and reconstruction technique to enhance radiograph properties, using a series of
morphological erosion and dilation to improve the visual quality of the chest radiographs for the detection of
cancer nodules.
This document proposes a new method called multi-surface fitting for enhancing the resolution of digital images. The method fits multiple surfaces, with one surface fitted for each low-resolution pixel, and then fuses the multi-sampling values from these surfaces using maximum a posteriori estimation. This allows more low-resolution pixel information to be utilized to reconstruct the high-resolution image compared to other interpolation-based methods. The method is shown to effectively preserve image details without requiring assumptions about the image prior, as iterative techniques do. It provides error-free high resolution for test images.
Estimation of Terrain Gradient Conditions & Obstacle Detection Using a Monocu...Connor Goddard
This document summarizes an investigation into using monocular vision to estimate terrain gradient and detect obstacles for autonomous robot navigation. It explores using optical flow analysis of images to model vertical displacement of features and detect discrepancies that could indicate obstacles or changes in terrain slope. Three experiments are described that test different template matching approaches to track feature movement across images captured at various robot speeds and terrain conditions. The goal is to develop models of expected vertical displacement under different speeds that can be compared to current observations to estimate motion and environmental factors.
Attenuation correction designed for PET/MR hybrid imaging frameworks along with portion making arrangements used for MR-based radiation treatment remain testing because of lacking high-energy photon weakening data. We present a new method so as to uses the learned nonlinear neighborhood descriptors also highlight coordinating toward foresee pseudo-CT pictures starting T1w along with T2w MRI information. The nonlinear neighborhood descriptors are acquired through anticipating the direct descriptors interested in the nonlinear high-dimensional space utilizing an unequivocal constituent guide also low-position guess through regulated complex regularization. The nearby neighbors of every near descriptor inside the data MR pictures are looked during an obliged spatial extent of the MR pictures among the training dataset. By that point, the pseudo-CT patches are evaluated through k-closest neighbor relapse. The planned procedure designed for pseudo-CT forecast is quantitatively broke downward on top of a dataset comprising of coordinated mind MRI along with CT pictures on or after 13 subjects.
Uncalibrated View Synthesis Using Planar Segmentation of Images ijcga
This paper presents an uncalibrated view synthesis method using piecewise planar regions that are extracted from a given set of image pairs through planar segmentation. Our work concentrates on a view synthesis method that does not need estimation of camera parameters and scene structure. For our goal, we simply assume that images of real world are composed of piecewise planar regions. Then, we perform view
synthesis simply with planar regions and homographies between them. Here, for accurate extraction of planar homographies and piecewise planar regions in images, the proposed method employs iterative homography estimation and color segmentation-based planar region extraction. The proposed method
synthesizes the virtual view image using a set of planar regions as well as a set of corresponding
homographies. Experimental comparisons with the images synthesized using the actual three-dimensional
(3-D) scene structure and camera poses show that the proposed method effectively describes scene changes by viewpoint movements without estimation of 3-D and camera information.
AUTOMATED MANAGEMENT OF POTHOLE RELATED DISASTERS USING IMAGE PROCESSING AND ...ijcsit
Potholes though seem inconsequential, may cause accidents resulting in loss of human life. In this paper, we present an automated system to efficiently manage the potholes in a ward by deploying geotagging and image processing techniques that overcomes the drawbacks associated with the existing
survey-oriented systems. Image processing is used for identification of target pothole regions in the 2D
images using edge detection and morphological image processing operations. A method is developed to
accurately estimate the dimensions of the potholes from their images, analyze their area and depth,estimate the quantity of filling material required and therefore enabling pothole attendance on a priority basis. This will further enable the government official to have a fully automated system for e f f e c t i v e l y ma n a g i ng pothole related disasters.
New Approach for Detecting and Tracking a Moving Object IJECEIAES
This article presents the implementation of a tracking system for a moving target using a fixed camera. The objective of this work is the ability to detect a moving object and locate their positions. In picture processing, tracking moving objects in a known or unknown environment is commonly studied. It is based on invariance properties of objects of interest. The invariance can affect the geometry of the scene or the objects. The proposed approach is composed of several steps; the first is the extraction of points of interest in the current image. Then, these points will be tracked in the following image by using techniques for calculating the optical flow. After this step, the static points will be removed to focus on moving objects, That is to say, there is only the characteristic points belonging to moving objects. Now, to detect moving targets using images of the video, the background is first extracted from the successive images. In our approach, a method of the average values of every pixel has been developed for modeling background. The last step which stays before switching to tracking moving object is the segmentation which allows identifying every moving object. And by using the characteristic points in the previous steps.
IRJET-A Review on Implementation of High Dimension Colour Transform in Domain...IRJET Journal
This document reviews algorithms for detecting salient regions in images using high dimensional color transforms. It summarizes several existing methods that use color contrast, frequency analysis, and superpixel segmentation. A key method discussed creates a saliency map by finding the optimal linear combination of color coefficients in a high dimensional color space. This allows more accurate detection of salient objects versus methods using only RGB color. The performance of this high dimensional color transform method is improved by also utilizing relative location and color contrast between superpixels as learned features.
This document summarizes a method for enhancing fingerprint images using short-time Fourier transform (STFT) analysis. The key steps are:
1. Performing STFT analysis on overlapping windows of the fingerprint image to estimate the local ridge orientation, frequency, and region mask in each window.
2. Using the estimated orientation and frequency values to filter each window of the fingerprint image in the Fourier domain for enhancement.
3. Reconstructing the enhanced fingerprint image by combining the results from each analyzed window.
VISUAL TRACKING USING PARTICLE SWARM OPTIMIZATIONcsandit
The problem of robust extraction of visual odometry from a sequence of images obtained by an
eye in hand camera configuration is addressed. A novel approach toward solving planar
template based tracking is proposed which performs a non-linear image alignment for
successful retrieval of camera transformations. In order to obtain global optimum a biometaheuristic
is used for optimization of similarity among the planar regions. The proposed
method is validated on image sequences with real as well as synthetic transformations and
found to be resilient to intensity variations. A comparative analysis of the various similarity
measures as well as various state-of-art methods reveal that the algorithm succeeds in tracking
the planar regions robustly and has good potential to be used in real applications.
A NEW APPROACH OF BRAIN TUMOR SEGMENTATION USING FAST CONVERGENCE LEVEL SETijbesjournal
Segmentation of region of interest has a critical task in image processing applications. The accuracy of Segmentation is based on processing methodology and limiting value used. In this paper, an enhanced approach of region segmentation using level set (LS) method is proposed, which is achieved by using cross over point in the valley point as a new dynamic stopping criterion in the level set segmentation. The proposed method has been tested with developed database of MR Images. From the test results, it is found that proposed method improves the convergence performance such as complexity in terms of number of iterations, delay and resource overhead as compared to conventional level set based segmentation
approach.
International Journal of Engineering Inventions (IJEI) provides a multidisciplinary passage for researchers, managers, professionals, practitioners and students around the globe to publish high quality, peer-reviewed articles on all theoretical and empirical aspects of Engineering and Science.
Robust Clustering of Eye Movement Recordings for QuantiGiuseppe Fineschi
Characterizing the location and extent of a viewer’s interest, in terms of eye movement recordings, informs a range of investigations in image and scene viewing. We present an automatic data-driven method for accomplishing this, which clusters visual point-of-regard (POR) measurements into gazes and regions-ofinterest using the mean shift procedure. Clusters produced using this method form a structured representation of viewer interest, and at the same time are replicable and not heavily influenced by noise or outliers. Thus, they are useful in answering fine-grained questions about where and how a viewer examined an image.
EVALUATION OF THE VISUAL ODOMETRY METHODS FOR SEMI-DENSE REAL-TIMEacijjournal
This document summarizes the evaluation of visual odometry methods for semi-dense real-time reconstruction. It discusses two popular visual odometry approaches, LSD-SLAM and ORB-SLAM2, and evaluates their performance on three datasets. It then proposes a new approach that combines feature-based and feature-less methods for real-time odometry with a stereo camera, matching images semi-densely and reconstructing 3D environments directly on pixels with gradients.
A study of a modified histogram based fast enhancement algorithm (mhbfe)sipij
Image enhancement is one of the most important issues in low-level image processing. The goal of image
enhancement is to improve the quality of an image such that enhanced image is better than the original
image. Conventional Histogram equalization (HE) is one of the most algorithms used in the contrast
enhancement of medical images, this due to its simplicity and effectiveness. However, it causes the
unnatural look and visual artefacts, where it tends to change the brightness of an images. The Histogram
Based Fast Enhancement Algorithm (HBFE) tries to enhance the CT head images, where it improves the
water-washed effect caused by conventional histogram equalization algorithms with less complexity. It
depends on using full gray levels to enhance the soft tissues ignoring other image details. We present a
modification of this algorithm to be valid for most CT image types with keeping the degree of simplicity.
Experimental results show that The Modified Histogram Based Fast Enhancement Algorithm (MHBFE)
enhances the results in term of PSNR, AMBE and entropy. We use also the Statistical analysis to ensure
the improvement of the proposed modification that can be generalized. ANalysis Of VAriance (ANOVA) is
used as first to test whether or not all the results have the same average. Then we find the significant
improvement of the modification.
Performance analysis of contourlet based hyperspectral image fusion methodijitjournal
Recently, contourlet transform has been widely used in hyperspectral image fusion due to its advantages,
such as high directionality and anisotropy; and studies show that the contourlet-based fusion methods
perform better than the existing conventional methods including wavelet-based fusion methods. Few studies
have been done to comparatively analyze the performance of contourlet-based fusion methods;
furthermore, no research has been done to analyze the contourlet-based fusion methods by focusing on
their unique transform mechanisms. In addition, no research has focused on the original contourlet
transform and its upgraded versions. In this paper, we investigate three different kinds of contourlet
transform: i) original contourlet transform, ii) nonsubsampled contourlet transform, iii) contourlet
transform with sharp frequency localization. The latter two transforms were developed to overcome the
major drawbacks of the original contourlet transform; so it is necessary and beneficial to see how they
perform in the context of hyperspectral image fusion. The results of our comparative analysis show that the
latter two transforms perform better than the original contourlet transform in terms of increasing spatial
resolution and preserving spectral information.
An efficient image segmentation approach through enhanced watershed algorithmAlexander Decker
This document proposes an efficient image segmentation approach combining an enhanced watershed algorithm and color histogram analysis. The watershed algorithm is applied to preprocessed images after merging the results with an enhanced edge detection. Over-segmentation issues are addressed through a post-processing step applying color histogram analysis to each segmented region, improving overall performance. The document provides background on image segmentation techniques, reviews related work applying watershed algorithms, and discusses challenges like over-segmentation that watershed approaches can face.
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Novel Framework for Navigation using Enhanced Fuzzy Approach with Sliding Mod...IJECEIAES
The reliability of any embedded navigator in advanced vehicular system depends upon correct and precise information of navigational data captured and processed to offer trustworthy path. After reviewing the existing system, a significant trade-off is explored between the existing navigational system and present state of controller design on various case studies and applications. The existing design of controller system for navigation using error-prone GPS/INS data doesn’t emphasize on sliding mode controller. Although, there has been good number of studies in sliding mode controller, it is less attempted to optimize the navigational performance of a vehicle. Therefore, this paper presents a novel optimized design of a sliding mode controller that can be effectively deployed on advanced navigational system. The study outcome was found to offer higher speed, optimal control signal, and lower error occurances to prove that proposed system offers reliable and optimized navigational services in contrast to existing system.
An Efficient Algorithm for the Segmentation of Astronomical ImagesIOSR Journals
This document proposes an efficient algorithm for segmenting celestial objects from astronomical images. The algorithm uses multiple preprocessing steps including removing bright point sources, stationary wavelet transform, total variation denoising, and adaptive histogram equalization. Level set segmentation is then used as the key technique for segmentation. Preprocessing helps overcome issues like noise, weak object edges, and low contrast. Level set segmentation can segment objects while retaining their texture and shape information for subsequent classification. The algorithm is tested on various celestial objects and shown to effectively segment them.
In the past decade, seismic tomography techniques have improved through more sophisticated methodologies and technologies. An important issue is the accuracy and density of residual move-out (RMO) picks used to derive tomographic velocity model updates. A new automated method allows for precise tracking of RMO on pre-stack depth-migrated gathers, enabling fast determination of dense, high-quality travel time residuals for seismic tomography. This leads to better resolution of small-scale anomalies and flatter pre-stack depth-migrated gathers, providing better-focused structural images.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
Chest radiograph image enhancement with wavelet decomposition and morphologic...TELKOMNIKA JOURNAL
Medical image processing algorithms significantly affect the precision of disease diagnostic
process. This makes it crucial to improve the quality of a medical image with the goal to enhance
perceivability of the points of interest in order to obtain accurate diagnosis of a patient. Despite the
reliance of various medical diagnostics on X-rays, they are usually plagued by dark and low contrast
properties. Sought-after details in X-rays can only be accessed by means of digital image processing
techniques, despite the fact that these techniques are far from being perfect. In this paper, we implement
a wavelet decomposition and reconstruction technique to enhance radiograph properties, using a series of
morphological erosion and dilation to improve the visual quality of the chest radiographs for the detection of
cancer nodules.
This document proposes a new method called multi-surface fitting for enhancing the resolution of digital images. The method fits multiple surfaces, with one surface fitted for each low-resolution pixel, and then fuses the multi-sampling values from these surfaces using maximum a posteriori estimation. This allows more low-resolution pixel information to be utilized to reconstruct the high-resolution image compared to other interpolation-based methods. The method is shown to effectively preserve image details without requiring assumptions about the image prior, as iterative techniques do. It provides error-free high resolution for test images.
Estimation of Terrain Gradient Conditions & Obstacle Detection Using a Monocu...Connor Goddard
This document summarizes an investigation into using monocular vision to estimate terrain gradient and detect obstacles for autonomous robot navigation. It explores using optical flow analysis of images to model vertical displacement of features and detect discrepancies that could indicate obstacles or changes in terrain slope. Three experiments are described that test different template matching approaches to track feature movement across images captured at various robot speeds and terrain conditions. The goal is to develop models of expected vertical displacement under different speeds that can be compared to current observations to estimate motion and environmental factors.
Attenuation correction designed for PET/MR hybrid imaging frameworks along with portion making arrangements used for MR-based radiation treatment remain testing because of lacking high-energy photon weakening data. We present a new method so as to uses the learned nonlinear neighborhood descriptors also highlight coordinating toward foresee pseudo-CT pictures starting T1w along with T2w MRI information. The nonlinear neighborhood descriptors are acquired through anticipating the direct descriptors interested in the nonlinear high-dimensional space utilizing an unequivocal constituent guide also low-position guess through regulated complex regularization. The nearby neighbors of every near descriptor inside the data MR pictures are looked during an obliged spatial extent of the MR pictures among the training dataset. By that point, the pseudo-CT patches are evaluated through k-closest neighbor relapse. The planned procedure designed for pseudo-CT forecast is quantitatively broke downward on top of a dataset comprising of coordinated mind MRI along with CT pictures on or after 13 subjects.
Uncalibrated View Synthesis Using Planar Segmentation of Images ijcga
This paper presents an uncalibrated view synthesis method using piecewise planar regions that are extracted from a given set of image pairs through planar segmentation. Our work concentrates on a view synthesis method that does not need estimation of camera parameters and scene structure. For our goal, we simply assume that images of real world are composed of piecewise planar regions. Then, we perform view
synthesis simply with planar regions and homographies between them. Here, for accurate extraction of planar homographies and piecewise planar regions in images, the proposed method employs iterative homography estimation and color segmentation-based planar region extraction. The proposed method
synthesizes the virtual view image using a set of planar regions as well as a set of corresponding
homographies. Experimental comparisons with the images synthesized using the actual three-dimensional
(3-D) scene structure and camera poses show that the proposed method effectively describes scene changes by viewpoint movements without estimation of 3-D and camera information.
AUTOMATED MANAGEMENT OF POTHOLE RELATED DISASTERS USING IMAGE PROCESSING AND ...ijcsit
Potholes though seem inconsequential, may cause accidents resulting in loss of human life. In this paper, we present an automated system to efficiently manage the potholes in a ward by deploying geotagging and image processing techniques that overcomes the drawbacks associated with the existing
survey-oriented systems. Image processing is used for identification of target pothole regions in the 2D
images using edge detection and morphological image processing operations. A method is developed to
accurately estimate the dimensions of the potholes from their images, analyze their area and depth,estimate the quantity of filling material required and therefore enabling pothole attendance on a priority basis. This will further enable the government official to have a fully automated system for e f f e c t i v e l y ma n a g i ng pothole related disasters.
New Approach for Detecting and Tracking a Moving Object IJECEIAES
This article presents the implementation of a tracking system for a moving target using a fixed camera. The objective of this work is the ability to detect a moving object and locate their positions. In picture processing, tracking moving objects in a known or unknown environment is commonly studied. It is based on invariance properties of objects of interest. The invariance can affect the geometry of the scene or the objects. The proposed approach is composed of several steps; the first is the extraction of points of interest in the current image. Then, these points will be tracked in the following image by using techniques for calculating the optical flow. After this step, the static points will be removed to focus on moving objects, That is to say, there is only the characteristic points belonging to moving objects. Now, to detect moving targets using images of the video, the background is first extracted from the successive images. In our approach, a method of the average values of every pixel has been developed for modeling background. The last step which stays before switching to tracking moving object is the segmentation which allows identifying every moving object. And by using the characteristic points in the previous steps.
IRJET-A Review on Implementation of High Dimension Colour Transform in Domain...IRJET Journal
This document reviews algorithms for detecting salient regions in images using high dimensional color transforms. It summarizes several existing methods that use color contrast, frequency analysis, and superpixel segmentation. A key method discussed creates a saliency map by finding the optimal linear combination of color coefficients in a high dimensional color space. This allows more accurate detection of salient objects versus methods using only RGB color. The performance of this high dimensional color transform method is improved by also utilizing relative location and color contrast between superpixels as learned features.
This document summarizes a method for enhancing fingerprint images using short-time Fourier transform (STFT) analysis. The key steps are:
1. Performing STFT analysis on overlapping windows of the fingerprint image to estimate the local ridge orientation, frequency, and region mask in each window.
2. Using the estimated orientation and frequency values to filter each window of the fingerprint image in the Fourier domain for enhancement.
3. Reconstructing the enhanced fingerprint image by combining the results from each analyzed window.
VISUAL TRACKING USING PARTICLE SWARM OPTIMIZATIONcsandit
The problem of robust extraction of visual odometry from a sequence of images obtained by an
eye in hand camera configuration is addressed. A novel approach toward solving planar
template based tracking is proposed which performs a non-linear image alignment for
successful retrieval of camera transformations. In order to obtain global optimum a biometaheuristic
is used for optimization of similarity among the planar regions. The proposed
method is validated on image sequences with real as well as synthetic transformations and
found to be resilient to intensity variations. A comparative analysis of the various similarity
measures as well as various state-of-art methods reveal that the algorithm succeeds in tracking
the planar regions robustly and has good potential to be used in real applications.
A NEW APPROACH OF BRAIN TUMOR SEGMENTATION USING FAST CONVERGENCE LEVEL SETijbesjournal
Segmentation of region of interest has a critical task in image processing applications. The accuracy of Segmentation is based on processing methodology and limiting value used. In this paper, an enhanced approach of region segmentation using level set (LS) method is proposed, which is achieved by using cross over point in the valley point as a new dynamic stopping criterion in the level set segmentation. The proposed method has been tested with developed database of MR Images. From the test results, it is found that proposed method improves the convergence performance such as complexity in terms of number of iterations, delay and resource overhead as compared to conventional level set based segmentation
approach.
Robust Human Tracking Method Based on Apperance and Geometrical Features in N...csandit
This paper proposes a robust tracking method which concatenates appearance and geometrical
features to re-identify human in non-overlapping views. A uniformly-partitioning method is
proposed to extract local HSV(Hue, Saturation, Value) color features in upper and lower
portion of clothing. Then adaptive principal view selecting algorithm is presented to locate
principal view which contains maximum appearance feature dimensions captured from different
visual angles. For each appearance feature dimension in principal view, all its inner frames get
involved in training a support vector machine (SVM). In matching process, human candidate
filtering is first operated with an integrated geometrical feature which connects height estimate
with gait feature. The appearance features of the remaining human candidates are later tested
by SVMs to determine the object’s existence in new cameras. Experimental results show the
feasibility and effectiveness of this proposal and demonstrate the real-time in appearance
feature extraction and robustness to illumination and visual angle change.
ROBUST HUMAN TRACKING METHOD BASED ON APPEARANCE AND GEOMETRICAL FEATURES IN ...cscpconf
This paper proposes a robust tracking method which concatenates appearance and geometrical
features to re-identify human in non-overlapping views. A uniformly-partitioning method is
proposed to extract local HSV(Hue, Saturation, Value) color features in upper and lower
portion of clothing. Then adaptive principal view selecting algorithm is presented to locate
principal view which contains maximum appearance feature dimensions captured from different
visual angles. For each appearance feature dimension in principal view, all its inner frames get
involved in training a support vector machine (SVM). In matching process, human candidate
filtering is first operated with an integrated geometrical feature which connects height estimate
with gait feature. The appearance features of the remaining human candidates are later tested
by SVMs to determine the object’s existence in new cameras. Experimental results show the
feasibility and effectiveness of this proposal and demonstrate the real-time in appearance
feature extraction and robustness to illumination and visual angle change.
Matching algorithm performance analysis for autocalibration method of stereo ...TELKOMNIKA JOURNAL
Stereo vision is one of the interesting research topics in the computer vision field. Two cameras are used to generate a disparity map, resulting in the depth estimation. Camera calibration is the most important step in stereo vision. The calibration step is used to generate an intrinsic parameter of each camera to get a better disparity map. In general, the calibration process is done manually by using a chessboard pattern, but this process is an exhausting task. Self-calibration is an important ability required to overcome this problem. Self-calibration required a robust and good matching algorithm to find the key feature between images as reference. The purpose of this paper is to analyze the performance of three matching algorithms for the autocalibration process. The matching algorithms used in this research are SIFT, SURF, and ORB. The result shows that SIFT performs better than other methods.
This document presents a method for tracking moving objects in video sequences using affine flow parameters combined with illumination insensitive template matching. The method extracts affine flow parameters from frames to model local object motion using affine transformations. It then applies template matching with illumination compensation to track objects across frames while being robust to illumination changes. The method is evaluated on various indoor and outdoor database videos and is shown to effectively track objects without false detections, handling issues like illumination variations, camera motion and dynamic backgrounds better than other methods.
A Review Paper on Stereo Vision Based Depth EstimationIJSRD
Stereo vision is a challenging problem and it is a wide research topic in computer vision. It has got a lot of attraction because it is a cost efficient way in place of using costly sensors. Stereo vision has found a great importance in many fields and applications in today’s world. Some of the applications include robotics, 3-D scanning, 3-D reconstruction, driver assistance systems, forensics, 3-D tracking etc. The main challenge of stereo vision is to generate accurate disparity map. Stereo vision algorithms usually perform four steps: first, matching cost computation; second, cost aggregation; third, disparity computation or optimization; and fourth, disparity refinement. Stereo matching problems are also discussed. A large number of algorithms have been developed for stereo vision. But characterization of their performance has achieved less attraction. This paper gives a brief overview of the existing stereo vision algorithms. After evaluating the papers we can say that focus has been on cost aggregation and multi-step refinement process. Segment-based methods have also attracted attention due to their good performance. Also, using improved filter for cost aggregation in stereo matching achieves better results.
An Exclusive Review of Popular Image Processing TechniquesChristo Ananth
Christo Ananth,"An Exclusive Review of Popular Image Processing Techniques", International Journal of Advanced Research in Management Architecture Technology & Engineering(IJARMATE),Volume 8,Issue 6,June 2022,pp:15-22.
Christo Ananth et al. discussed about a review paper which brings out a summary of popular image processing techniques in practice for students, faculty members and researchers in medical image processing field. Through Image processing, we do some operations on an image, to get an enhanced image or we try to acquire some useful information from it. They help in manipulating digital images through the use of computers. We Perform Image Restoration, Linear Filtering, Independent Component Analysis, Pixelation, Template Matching, Image Generation Techniques even to image to obtain promisable results. This Review Paper also summarizes some of the enhancement approaches which have impacted image segmentation approaches over these years.
RANSAC BASED MOTION COMPENSATED RESTORATION FOR COLONOSCOPY IMAGESsipij
Colonoscopy is a procedure that has been used widely to detect the abnormality in a colon. Colonoscopy images suffer from a lot of problems that make it hard for the doctor to investigate/ understand a colon patient. Unfortunately, with the current technology, three is no way for doctors to know if the whole colon surface has been investigated or not. We have developed a method that utilizes RANSAC-based image registration to align sequences of any length in the colonoscopy video and restores each frame of the video using information from these aligned images. We proposed two methods. First method used the deep neural net for the classification of informative and non-informative image. The classification result was used as a preprocessing for alignment method. Also, we proposed a visualization structure for the classification results. The second method used the alignment to decide/classify the bad and good alignment by using two factors. The first factor is the accumulated error and the second factor contain three checking steps that check the pair error alignment beside the geometry transform status. The second method was able to align long sequences.
To identify the person using gait knn based approacheSAT Journals
Abstract In human identification, the process of identifying the person by their gait is an emerging research trend in the field of visual surveillance. Gait is a new biometrics, has been recently used to recognize a person via style of his walking. While person walking variation becomes take place in different parts of body. On the basis of these variations, proposed method is evaluated on CASIA gait database by using K-nearest Neighbor classifier. Experimental results demonstrate that the proposed method has an encouraging recognition performance also the results indicate that the classification ability of KNN with correlation measure perform better than with other type of distance measure functions. Keywords: Gait biometrics, KNN, visual surveillance, CASIA, silhouette.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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A Parametric Approach to Gait Signature Extraction for Human Motion Identific...CSCJournals
The extraction and analysis of human gait characteristics using image sequences are currently an intense area of research. Identifying individuals using biometric methods has recently gained growing interest from computer vision researchers for security purposes at places like airport, banks etc. Gait recognition aims essentially to address this problem by identifying people at a distance based on the way they walk i.e., by tracking a number of feature points or gait signatures. We describe a new model-based feature extraction analysis is presented using Hough transform technique that helps to read the essential parameters used to generate gait signatures that automatically extracts and describes human gait for recognition. In the preprocessing steps, the picture frames taken from video sequences are given as input to Canny edge detection algorithm which helps to detect edges of the image by extracting foreground from background also it reduces the noise using Gaussian filter. The output from edge detection is given as input to the Hough transform. Using the Hough transform image, a clear line based model is designed to extract gait signatures. A major difficulty of the existing gait signature extraction methods are the good tracking the requisite feature points. In the proposed work, we have used five parameters to successfully extract the gait signatures. It is observed that when the camera is placed at 90 and 270 degrees, all the parameters used in the proposed work are clearly visible. The efficiency of the model is tested on a variety of body position and stride parameters recovered in different viewing conditions on a database consisting of 20 subjects walking at both an angled and frontal-parallel view with respect to the camera, both indoors and outdoors and find the method to be highly successful. The test results show good clarity rates, with a high level of confidence and it is suggested that the algorithm reported here could form the basis of a robust system for monitoring of gait.
Symbolic representation and recognition of gait an approach based on lbp of ...sipij
Gait is one of the biometric techniques used to identify an individual from a distance by his/her walking
style. Gait can be recognized by studying the static and dynamic part variations of individual body contour
during walk. In this paper, an interval value based representation and recognition of gait using local
binary pattern (LBP) of split gait energy images is proposed. The gait energy image (GEI) of a subject is
split into four equal regions. LBP technique is applied to each region to extract features and the extracted
features are well organized. The proposed representation technique is capable of capturing variations in
gait due to change in cloth, carrying a bag and different instances of normal walking conditions more
effectively. Experiments are conducted on the standard and considerably large database (CASIA database
B) and newly created University of Mysore (UOM) gait dataset to study the efficacy of the proposed gait
recognition system. The proposed system being robust to handle variations has shown significant
improvement in recognition rate.
Abstract— The division is the urgent stage in iris acknowledgment. We have utilized the worldwide limit an incentive for division. In the above calculation we have not considered the eyelid and eyelashes relics, which corrupt the execution of iris acknowledgment framework. The framework gives sufficient execution likewise the outcomes are attractive. Assist advancement of this technique is under way and the outcomes will be accounted for sooner rather than later. Based on the reasonable peculiarity of the iris designs we can anticipate that iris acknowledgment framework will turn into the main innovation in personality verification.In this paper, iris acknowledgment calculation is depicted. As innovation advances and data and scholarly properties are needed by numerous unapproved work force. Therefore numerous associations have being scanning routes for more secure confirmation strategies for the client get to. The framework steps are catching iris designs; deciding the area of iris limits; changing over the iris limit to the binarized picture; The framework has been actualized and tried utilizing dataset of number of tests of iris information with various complexity quality.
Keywords— GAC, Iris Recognition, Iris Segmentation, Snakes.
Medical Image Segmentation Based on Level Set MethodIOSR Journals
This document presents a new medical image segmentation technique based on the level set method. The technique uses a combination of thresholding, morphological erosion, and a variational level set method. Thresholding is applied to determine object pixels, followed by optional erosion to remove small fragments. Then a variational level set method is applied on the original image to evaluate the contour and segment objects. The technique is tested on various medical images and provides good segmentation results, though it struggles with complex images containing multiple distinct objects.
Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marc...Waqas Tariq
For navigation in complex environments, a robot needs to reach a compromise between the need for having efficient and optimized trajectories and the need for reacting to unexpected events. This paper presents a new sensor-based Path Planner which results in a fast local or global motion planning able to incorporate the new obstacle information. In the first step the safest areas in the environment are extracted by means of a Voronoi Diagram. In the second step the Fast Marching Method is applied to the Voronoi extracted areas in order to obtain the path. The method combines map-based and sensor-based planning operations to provide a reliable motion plan, while it operates at the sensor frequency. The main characteristics are speed and reliability, since the map dimensions are reduced to an almost unidimensional map and this map represents the safest areas in the environment for moving the robot. In addition, the Voronoi Diagram can be calculated in open areas, and with all kind of shaped obstacles, which allows to apply the proposed planning method in complex environments where other methods of planning based on Voronoi do not work.
This document summarizes a research article that proposes using a Bayesian classifier to aid in level set segmentation for early detection of diabetic retinopathy. Level set segmentation is used to segment retinal images and detect small blood clots. A Bayesian classifier is applied to help propagate the level set contour and classify pixels as normal blood vessels or abnormal blood clots. The method was tested on retinal images and showed it could detect small clots of 0.02mm, indicating it may help detect early proliferation stages. Results demonstrated it outperformed other methods in detecting minute clots for early stage proliferation detection.
Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. The purpose of this paper is the level set approach to simultaneous tissue segmentation and bias correction of Magnetic Resonance Imaging (MRI) images. A modified level set approach to joint segmentation and bias correction of images with intensity in homogeneity. A sliding window is used to transform the gradient intensity domain to another domain, where the distribution overlap between different tissues is significantly suppressed. Tissue segmentation and bias correction are simultaneously achieved via a multiphase level set evolution process. The proposed methods are very robust to initialization, and are directly compatible with any type of level set implementation. Experiments on images of various modalities demonstrated the superior performance over state-of-the-art methods.
Feature selection for sky image classification based on self adaptive ant col...IJECEIAES
Statistical-based feature extraction has been typically used to purpose obtaining the important features from the sky image for cloud classification. These features come up with many kinds of noise, redundant and irrelevant features which can influence the classification accuracy and be time consuming. Thus, this paper proposed a new feature selection algorithm to distinguish significant features from the extracted features using an ant colony system (ACS). The informative features are extracted from the sky images using a Gaussian smoothness standard deviation, and then represented in a directed graph. In feature selection phase, the self-adaptive ACS (SAACS) algorithm has been improved by enhancing the exploration mechanism to select only the significant features. Support vector machine, kernel support vector machine, multilayer perceptron, random forest,
k-nearest neighbor, and decision tree were used to evaluate the algorithms. Four datasets are used to test the proposed model: Kiel, Singapore whole-sky imaging categories, MGC Diagnostics Corporation, and greatest common divisor. The SAACS algorithm is compared with six bio-inspired benchmark feature selection algorithms. The SAACS algorithm achieved classification accuracy of 95.64% that is superior to all the benchmark feature selection algorithms. Additionally, the Friedman test and Mann-Whitney U test are employed to statistically evaluate the efficiency of the proposed algorithms.
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...IOSR Journals
Abstract: We investigated the Classification of satellite images and multispectral remote sensing data .we
focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for
classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area
and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian
filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using
proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM
classifier. The goal of this research is to provide the efficiency in classification of satellite images using the
object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the
proposed work is compared to FCM based classification. The results showed that the proposed technique has
achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle
classification respectively.
Keywords:-Satellite image, FCM Clustering, Classification, SVM classifier.
Similar to VISUAL TRACKING USING PARTICLE SWARM OPTIMIZATION (20)
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
This document discusses using social media technologies to promote student engagement in a software project management course. It describes the course and objectives of enhancing communication. It discusses using Facebook for 4 years, then switching to WhatsApp based on student feedback, and finally introducing Slack to enable personalized team communication. Surveys found students engaged and satisfied with all three tools, though less familiar with Slack. The conclusion is that social media promotes engagement but familiarity with the tool also impacts satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The document proposes a blockchain-based digital currency and streaming platform called GoMAA to address issues of piracy in the online music streaming industry. Key points:
- GoMAA would use a digital token on the iMediaStreams blockchain to enable secure dissemination and tracking of streamed content. Content owners could control access and track consumption of released content.
- Original media files would be converted to a Secure Portable Streaming (SPS) format, embedding watermarks and smart contract data to indicate ownership and enable validation on the blockchain.
- A browser plugin would provide wallets for fans to collect GoMAA tokens as rewards for consuming content, incentivizing participation and addressing royalty discrepancies by recording
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This document discusses the importance of verb suffix mapping in discourse translation from English to Telugu. It explains that after anaphora resolution, the verbs must be changed to agree with the gender, number, and person features of the subject or anaphoric pronoun. Verbs in Telugu inflect based on these features, while verbs in English only inflect based on number and person. Several examples are provided that demonstrate how the Telugu verb changes based on whether the subject or pronoun is masculine, feminine, neuter, singular or plural. Proper verb suffix mapping is essential for generating natural and coherent translations while preserving the context and meaning of the original discourse.
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The document discusses automated penetration testing and provides an overview. It compares manual and automated penetration testing, noting that automated testing allows for faster, more standardized and repeatable tests but has limitations in developing new exploits. It also reviews some current automated penetration testing methodologies and tools, including those using HTTP/TCP/IP attacks, linking common scanning tools, a Python-based tool targeting databases, and one using POMDPs for multi-step penetration test planning under uncertainty. The document concludes that automated testing is more efficient than manual for known vulnerabilities but cannot replace manual testing for discovering new exploits.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
The document proposes a new validation method for fuzzy association rules based on three steps: (1) applying the EFAR-PN algorithm to extract a generic base of non-redundant fuzzy association rules using fuzzy formal concept analysis, (2) categorizing the extracted rules into groups, and (3) evaluating the relevance of the rules using structural equation modeling, specifically partial least squares. The method aims to address issues with existing fuzzy association rule extraction algorithms such as large numbers of extracted rules, redundancy, and difficulties with manual validation.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
2. 280 Computer Science & Information Technology (CS & IT)
robot and map the environment for a longer period of time. Since finding correspondences is
itself an error-prone task, a large portion of the error is introduced in a very early phase of motion
estimation.
There is another range of methods that utilize all pixels of an image region when calculating
camera displacement by aligning image regions and hence enjoy higher accuracy due to
exploitation of all possible visual information present in the segments of a scene[12]. These
methods are termed “direct image alignment” based approaches for motion estimation because
they do not have feature extraction and correspondence calculation steps and work directly on
image patches. Direct methods are often avoided due to their computational expense which
overpowers the benefits of accuracy they might provide, however an intelligent selection of the
important parts of the scene that are rich in visual information can provide a useful way of dealing
with the issue [13]. In addition to being direct in their approach, such methods can also better
exploit the geometrical structure of the environment by including rigidity constraints early in the
optimization process. The use of all visual information in a region of an image and keeping track
of gain or loss in subsequent snapshots of a scene is also relevant, since it is the way some
biological species navigate. For example, there are evidences that desert ants use the amount of
visual information which is common between a current image and a snapshot of the ant pit to
determine their way to the pit [14].
An important step in a direct image alignment based motion estimation approach is the
optimization of similarity among image patches. The major optimization technique that is
extensively used for image alignment is gradient descent although a range of algorithms (e.g.
Gauss-Newton, Newton-Raphson and Levenberg-Marquardt [9][15]) are used for calculation of a
gradient descent step. Newton’s method provides a high convergence rate because it is based on
second order Taylor series approximation, however, Hessian calculation is a computationally
expensive task. Moreover, a Hessian can also be indefinite, resulting in convergence failure.
These methods perform a linearization of the non-linear problem which can then be solved by
linear-least square methods. Since these methods are based on gradient descent, and use local
descent to determine the direction of an optimum in the search space, they have a tendency to get
stuck in the local optimum if the objective function has multiple optima. There are, however,
some bio-inspired metaheuristics that mimic the behavior of natural organisms (e.g. Genetic
Algorithms (GAs) and Particle Swarm Optimization (PSO) [16]-[18]) or the physical laws of
nature to cater this problem [19]. These methods have two common functionalities: exploration
and exploitation. During an exploration phase, like any organism explores its environment, the
search space is visited extensively and is gradually reduced over a period of iterations. The
exploitation phase comes in the later part of a search process, when the algorithm converges
quickly to a local optimum and the local optimum is accepted as the global best solution. This
two-fold strategy provides a solid framework for finding the global optimum and avoiding the
local best solution at the same time. In this case, PSO is interesting as it mimics the navigation
behavior of swarms, especially colony movement of honeybees if an individual bee is represented
as a particle which has an orientation and is moving with a constant velocity. Arbitrary motion in
the initial stage of the optimization process ensures better exploration of the search space and a
consensus among the particles reflects better convergence.
In this paper, the aim is to solve the problem of camera motion estimation by directly tracking
planar regions in images. In order to learn an accurate estimate of motion and to embed the
rigidity constraint of the scene in the optimization process, a PSO based camera tracking is
performed which uses a non-linear image alignment based approach for finding the displacement
of camera within subsequent images. The major contributions of the paper are: a) a novel
approach to planar template based camera tracking technique which employs a bio-metaheuristic
3. Computer Science & Information Technology (CS & IT) 281
for solving optimization problem b) Evaluation of the proposed method using multiple similarity
measures and a comparative performance analysis of the proposed method.
The rest of the paper is organized as follows: In section 2 the most relevant studies are listed, in
section 3 the details of the method are described, section 4 explains the experimental setup and
discussion of the results, and section 5 presents the conclusion and potential future work.
2. RELEVANT WORK
There are many studies that focus on feature oriented camera motion estimation by tracking a
template in the images. However, here we focus on the direct methods that track a planar template
by optimizing the similarity between a reference and a current image. A classic example of such a
direct approach toward camera motion estimation is the use of a brightness constancy assumption
during motion and is linked to optical flow measurement [12]. Direct methods based on optical
flow were later divided into two major pathways: Inverse Compositional (IC) and Forward
Compositional (FC) approaches [20]-[22]. The FC approaches solve the problem by estimating
the iterative displacement of warp parameters and then updating the parameters by incrementing
the displacement. IC approaches, on the other hand, solve the problem by updating the warp with
an inverted incremental warp. These methods linearize the optimization problem by Taylor-series
approximation and then solve it by least-square methods. In [23] a multi-plane estimation method
along with tracking is proposed in which region-based planes are firstly detected and then the
camera is tracked by minimizing the SSD (Sum of Squared Differences) between respective
planar regions in 2D images. Another example of direct template tracking is [23] which improves
the tracking method by replacing the Jacobian approximation in [21] with a Hyper-plane
Approximation. The method in [23] is similar to our method because it embeds constraints in a
non-linear optimization process (i.e. Levenberg-Marquardt [9]) although it differs from the
method proposed here since the latter employs a bio-inspired metaheuristic based optimization
process which maximizes the mutual information in-between images and also the proposed
method does not use constraints among the planes.
3. METHODOLOGY
The problem that is being addressed deals with estimation of a robot’s state at a given time step
that satisfies the planarity constraint. Let ࢞(ݔ௧
, ݔ
) ∈ ℝ
be the state of the robot with ݔ௧
∈
ℝଷ
, ݔ
∈ ℝଷ
being the position and orientation of the robot in Euclidean space. Let’s also
consider ,ܫ ܫ to be the current and reference image, respectively. If the current image rotates
ࡾ ∈ ॺॹ(3) and translates ݐ ∈ ℝଷ
from the reference image in a given time step then the motion
in terms of homogeneous representation ࢀ ∈ ॺॱ(3) can be given as:
ࢀ(࢞) =
̂ݏ(࢞࢘) ࢚࢞
ࢀ
1
൨ (1)
where ̂ݏ is the skew symmetric matrix. It is indeed this transformation that we ought to recover
given the current state of the robot and reference template image.
3.1 Plane Induced Motion
It is often the case that the robot’s surrounding is composed of planar components, especially in
the case of indoor navigation where most salient landmarks are likely to be planar in nature. In
such cases the pixels in an image can be related to the pixels in the reference image by a
projective homography H that represents the transformation between the two images [24] If
4. 282 Computer Science & Information Technology (CS & IT)
= ሾ,ݑ ,ݒ 1ሿ்
be the homogeneous coordinates of the pixel in an image and
= ሾݑ
, ݒ
, 1ሿ்
be
the homogenous coordinates of the reference image then the relationship between the two set of
pixels can be written as given in equation 2.
∝ ࡴ࢘
(2)
Let’s now consider that the plane that is to be tracked or the plane which holds a given landmark
has a normal ࢘ ∈ ℝଷ, which has its projection in the reference image ܫ. In case of a calibrated
camera, the intrinsic parameters, which are known, can be represented in terms of a matrix
ࡷ ∈ ℝଷ௫ଷ
. If the 3D transformation between the frames is ࢀ, then the Euclidean homography
with a non-zeros scale factor can be calculated as:
ࡴ(ࢀ, ࢘) ∝ ࡷ൫ࡾ + ࢚࢘
ࢀ
൯ࡷି
(3)
3.2 Model-Based Image Alignment
The next step after modeling the planarity of the scene is to relate plane transformations in the 3D
scene to their projected transformations in the images. For that reason a general mapping function
that transforms a 2D image given a projective homography can be represented by a warping
operator w and is defined as follows:
࢝(ࡴ, ࢘) = ቂ
భభ௨ೝାభమ௩ೝାభయ
యభ௨ೝାయమ௩ೝାయయ
,
మభ௨ೝାమమ௩ೝାమయ
యభ௨ೝାయమ௩ೝାయయ
ቃ
ࢀ
(4)
If the normal of the tracked plane is known then the problem to be addressed is that of metric
model based alignment or simply model based non-linear image alignment. It is the
transformation ࢀ ∈ ॺॱ(3)that is to be learned by warping the reference image and measuring the
similarity between the warped and the current image. Since the intensity of a pixel ࡵ() is a non-
linear function, we need a non-linear optimization procedure. More formally, the task is to learn
an optimum transformation ࢀ = ࢀ(࢞)that maximizes the following:
max୶∈ℝల ࣒ ൬ࡵ࢘ ቀ࢝൫ࡴ൫ܶ, ୰൯, ࢘൯ቁ , ࡵ()൰ (5)
where ࣒ is a similarity function and ܶ is updated as ܶ ⇠ ܶ(ܶ)ݔ for every new image in the
sequence.
3.3 Similarity Measure
In order for any optimization method to work effectively and efficiently, the search space needs
to be modeled in such a way that it captures the multiple optima of a function but at the same time
suppresses local optima by enhancing the global optimum. It is also important that such modeling
of similarity must provide enough convergence space so that the probability of missing the global
optimum is minimized. This job is performed by a selection of similarity measure that is best
suited for a given problem context. An often used measure is SSD (Sum of Squared Differences)
that can be given as:
࣒ࡿࡿࡰ = ∑ ቀࡵ࢘൫࢝(ࡴ, ࢘)൯ − ࡵ()ቁ
ࡺ
(6)
5. Computer Science & Information Technology (CS & IT) 283
where ‘N’ is the total number of pixels in a tracked region of the image.
Similarly, another relevant similarity measure is the cross correlation coefficient of the given two
data streams. Often a normalized version is used to restrict the comparison space to the range [0,
1]. The normalized cross correlation between a current image patch ܫ and a reference image patch
ܫ, with ߤ, ߤ being their respective means, can be written as:
࣒ࡺ = ∑
(ࡵ࢘(,)ିࣆ࢘ )(ࡵ(,)ିࣆࡵ )
ට(ࡵ࢘
(,)ିࣆ࢘ )(ࡵ(,)ିࣆࡵ )
, (7)
Fig. 1: Convergence surface of various similarity functions along with motion of a PSO particle on its way
towards convergence depicted by green path. First Row: Sum of squared difference (log(࣒ࡿࡿࡰ)ି
),
Second Row: Normalized Cross Correlation (࣒ࡺ), Third Row: Mutual Information (࣒ࡹࡵ).
The similarity measures presented in equation 6 and 7 have the ability to represent the amount of
information that is shared by the two data streams; however, as can be seen in figure 1, the
convergence space and the emphasis on the global optimum need improvement. A more intuitive
approach for measuring similarity among the data is Mutual Information (MI), taken from
information theory, that measures the amount of data that is shared between the two input data
streams [25] .The application of MI in image alignment tasks and its ability to capture the shared
information have also proven to be successful [26],[27] The reason for avoidance of MI in
robotics tasks has been its relatively higher computational expense, since it involves histogram
6. 284 Computer Science & Information Technology (CS & IT)
computation. However, the gains are more than the losses, so we choose to use MI as our main
similarity measure. Formally, the MI between two input images can be computed as:
where ࡱ(ࡵ), ࡱ(ࡵ࢘, ࡵ), ܰூ are the entropy, joint entropy and maximum allowable intensity value
respectively.
Entropy according to Shannon [23] is the measure of variability in a random variable I, whereas
‘i’ is the possible value of I and ߩூ(݅) = Pr (݅ == ࡵ()) is the probability density function. The
log basis only scales the unit of entropy and does not influence the optimization process. In other
words, since −݈ߩ(݃ூ(݅)) represents the measure of uncertainty of an event ‘i’ and ࡱ(ࡵ) is the
weighted mean of the uncertainties, the latter represents the variability of random variable I. In
the case of images we have pixel intensities as possible values of a random variable, so the
probability density function of a pixel value can be estimated by a normalized histogram of the
input image. The entropy for the input image is therefore a dispersion measure of the normalized
histogram. This is the same in the case of joint entropy since there are two variables involved, so
the joint probability density function is ߩஈ(݅, ݆) = Pr (݅ == ࡵ࢘(࢘) ∩ ݆ == ࡵ()) where i, j are
possible values of image ࡵ, ࡵ respectively. The joint entropy measures the dispersion in the joint
histogram. This dispersion provides a similarity measure because when the dispersion in the joint
histogram is small then the correlation among the images is strong, giving a large value of MI and
suggesting that two images are aligned, while in case of large dispersions, MI would have a small
value and images would be unaligned.
3.4 Optimization Procedure
The problem of robust retrieval of Visual Odometry (VO) in subsequent images is challenging
due to the non-linear and continuous nature of the huge search space. The non-linearity is
commonly tackled using linearization of the problem function; however, this approximation is not
entirely general due to challenges in exact modeling of image intensity. Another route to solve the
problem is to use non-linear optimization such as Newton Optimization which gives fairly good
convergence due to the fact that it is based on Taylor series approximation of the similarity
function. However, it requires computation of the Hessian which is computationally expensive
and also it must be positive definitive for a convergence to take place.
The proposed method seeks the solution to the optimization problem presented in equation 5. In
order to find absolute global extrema and not get stuck in local extrema we choose a bio-inspired
metaheuristic optimization approach (i.e. PSO). Particle Swarm Optimization (PSO) is an
evolutionary algorithm which is directly inspired by the grouping behavior of social animals,
notably in the shape of bird flocking, fish schooling and bee swarming. The primary reason for
interest in learning and modeling the science behind such activities has been the remarkable
ability possessed by natural organisms to solve complex problems (e.g scattering, regrouping,
maintaining course, etc.) in a seamlessly and robust fashion. The generalized encapsulation of
such behaviors opens up horizons for potential applications in nearly any field. The range of
problems that can be solved range from resource management tasks (e.g intelligent planning and
scheduling) to real mimicked behaviors by robots. The particles in a swarm move collectively by
࣒ࡹࡵ = ࡱ(ࡵ) + ࡱ(ࡵ࢘) − ࡱ(ࡵ࢘, ࡵ)
ࡱ(ࡵ) = − ∑ ߩ
ே
ୀ ூ
(݅)݈ߩ(݃ூ(݅))
ࡱ(ࡵ࢘, ࡵ) = − ∑ ∑ ߩ
ே
ୀ ஈ
(݅, ݆)݈ߩ(݃ஈ(݅, ݆))
ே
ୀ
(8)
7. Computer Science & Information Technology (CS & IT) 285
keeping a safe distance from other members in order to avoid obstacles while moving in a
consensus direction to avoid predators while maintaining a constant velocity. This results in
behavior in which a flock/swarm moves towards a common goal (e.g. a Hive, food source) while
intra-group motion seems random. This makes it difficult for predators to focus on one prey while
it also helps swarms to maintain their course, especially in case of long journeys that are
common, e.g., for migratory birds. The exact location of the goal is usually unknown as it is in the
case of any optimization problem where the optimum solution is unknown. A pictorial diction of
the robot’s states represented as particles in an optimization process can be seen in figure 2.
Fig. 2: A depiction of PSO particles (i.e. robot states) taking part in an optimization process. Blue arrows
show the velocity of a particle and the local best solution is highlighted by an enclosing circle.
PSO is implemented in many ways with varying levels of bioinspiration reflected in terms of the
neighborhood topology that is used for convergence [28]. Each particle maintains it current best
position ௦௧ and global best ݃௦௧ position. The current best position is available to every
particle in the swarm. A particle updates its position based on its velocity, which is periodically
updated with a random weight. The particle that has the best position in the swarm at a given
iteration attracts all other particles towards itself. The selection of attracted neighborhood as well
as the force to which the particles are attracted depends on the topology being used. Generally a
PSO consists of two broad functions: one for exploration and one for exploitation. The degree and
extend of time that each function is performed depends again on the topology being used. A
common model of PSO allows more exploration to be performed in the initial iterations while it is
gradually decreased and a more localized search is performed in the later iterations of the
optimization process. There can be multiple topologies, some of which are presented in figure 3.
A global topology considers all the particles in the swarm and thus converges faster but
potentially to a local optimum. The local best topology provides relatively more freedom and only
a set of close neighbors are allowed to be attracted to the best particle in the swarm. This allows
the algorithm to converge more slowly, but chances of finding the global optimum are increased.
Another subtle variation to this neighborhood selection could be that it is performed dynamically,
e.g. being increased over the number of iterations, making the system more explorative in the
start and more exploitative in later stages of convergence so that a global optimum is achieved.
ݔଵ
ݔଶ
௧
ݔଷ
௧
ݔଵ
௧
ݔଶ
ݔଷ
8. 286 Computer Science & Information Technology (CS & IT)
Fig. 3: Various topologies used in PSO based optimization processes. (a) Global topology uses all
neighbors, (b) Circle Topology consider 2-neighbours, (c) Local Topology uses k-neighbors, and (d) Wheel
Topology considers only immediate neighbors.
The process of PSO optimization starts with initialization of the particles. Each particle is
initialized with a random initial velocity ࢜ and random current position ࢞ represented by a k
dimensionalvector where ‘k’ is the number of degrees of freedom of the solution. The search
space is discretized and limited with a boundary constraint |࢞| ≤ ࢈, ࢈ ∈ ሾܾ, ܾ௨ሿ where ܾ, ܾ௨
are lower and upper bounds of motion in each dimension. This discretization and application of
boundary constraints helps reduce the search space assuming that the motion in between
subsequent frames is not too large. After initialization, particles are moved arbitrarily in the
search space to find the solution that maximizes the similarity value as given in equation 8. Each
particle updates its position based on its own velocity and the position of the best particle in the
neighborhood. The position and velocity update is given in equation 9:
where ω is the inertial weight and is used to control the momentum of the particles. When a large
value of inertial weight is used, particles are influenced more by their last velocity and collisions
might happen with very large values. The cognitive (or self- awareness) component of the
velocity update is represented by ࢉ = ࢞
࢈
− ࢞()ݐwhere ࢞
࢈
is the personal best solution of the
particle. Similarly, the social component is represented as ࢙ = ࢞
ࢍ
− ࢞()ݐwhere ࢞
ࢍ
is the best
solution in the particle’s neighborhood. Randomness is achieved by ࣌ࢉ, ࢙࣌ ∈ ሾ0,1ሿ for cognitive
and social components respectively. The constant weights ߙ, ߙ௦ control the influence of each
component in the update process. The final step in the PSO algorithm is evaluating against the
termination criteria. There could be a range of strategies for terminating the algorithm: a) using a
fixed number of iterations until improvement in the solution is observed, b) reaching a maximum
number of consecutive iterations with no change in the solution, c) exceeding a threshold on the
maximum similarity value. The first strategy would be fast but may fail to return a true optimum
due to being deceived by a local best solution. The second strategy would be better than the first
one in returning the global best solution; however it might also not guarantee the best solution.
Third strategy is good in the sense that it can find the best solution, but it could make the
algorithm continue for an infinite number of iterations. There is no general strategy for
termination; rather, each problem situation needs to be adapted. If faster computation is the
ultimate concern then the first option would be a good choice; similarly the third option would be
࢞(ݐ + 1) = ࢞()ݐ + ࢜(ݐ + 1)
࢜(ݐ + 1) = ࣓ ࢜()ݐ + ߙߪࢉ + ߙ௦ߪ௦࢙
(a) (b)
(c) (d)
(9)
9. Computer Science & Information Technology (CS & IT) 287
good in situations where the best solution is to be found regardless of the time taken. A weighted
combination of termination criteria proves better suited for the problem at hand.
3.5 Tracking Method
The proposed plane tracking method consists of three main steps: initialization, tracking and
updating. These steps are given as follows:
1) The planar area in the image that is to be tracked is initialized in the first frame and an
initial normal of the plane is provided. If the plane normal is not already known then a
rough estimate of the plane in the camera coordinate frame is given. The search space of
the problem is discretized and constrained within an interval. PSO is initialized with a
random solution and a suitable similarity function is provided.
2) The marked region in the template image is aligned with a region in the current image
and an optimum solution of the 6-dof transformation is obtained. The optimization
process continues until it meets one of the following conditions: (i) max number of
iterations is reached, (ii) the solution has not improved in a number of consecutive
iterations, or (iii) a threshold for solution improvement is reached.
3) The global camera transformation is updated and process repeats.
4. EXPERIMENTAL RESULTS
In order for the system to be evaluated the experiment setup must consider the basic assumptions
namely: planarity nature of the scene and small subsequent motion. The planarity nature of the
scene means that there should be a dominant plane in front of the camera whose normal is either
estimated by using another technique or using an approximated unit normal without scale,
however the rate of convergence and efficiency is affected in the latter case. The second
important assumption of the system is that the amount of motion in subsequent frames is small as
large motions increase the search space significantly. In addition to this, the planar region that is
to be tracked must be textured so as to provide good variance of similarity while being
transformed. Keeping these assumptions in mind, the algorithm is evaluated for both simulated
and real robotic transformations and results are recorded.
4.1 Synthetic sequence
The experimentation process using a benchmark tracking sequence is performed. The sequence
consists of a real image with a textured plane facing the camera and its 100 transformed
variations whereas the motion within the subsequent transformation is kept small. The tracking
region is marked in the template image in order to select the plane and the optimization algorithm
is initialized. The tracking method succeeds in capturing the motion as it can be seen in figure 4.
In order to test behavior of the similarity measures, the method is repeated with all three
similarity functions and error surface is analyzed which can be seen in the figure 1 which also
show the path of a particle in the swarm on its way toward convergence. It was found that MI
provides better convergence surface than other two participating similarity measures and hence it
is used for later stages of the evaluation process.
As it could be interesting to determine whether the algorithm could cater variation in the degree
of freedom, the sequence is run multiple times with different dimensions of the solution that is to
be learned. The increase in the number of parameters to be learned affects the convergence rate,
however the algorithm successfully converges all the variations as can be seen in the figure 5.
However, with an increase in degree of freedom, the search space expands exponentially making
it harder to converge in the same number of iterations as needed for lower degrees of freedom.
10. 288 Computer Science & Information Technology (CS & IT)
This can be catered by multiple ways; a) increasing the overall number of iterations needed by the
algorithm to converge b) increasing the number of iterations dedicated for exploration and c)
putting more emphasis on the exploration by setting the appropriate inertial and social weights in
equation 9. The learning of all 6 parameters of a robotic motion is a challenging task and is often
reduced to 4 DOF by supplementing the two DOF from the IMU however using the optimization
based approach such as presented in this paper could be used for such challenging tasks and could
successfully solve the problem. It is important to note here that the proposed method only uses
single plane and hence only one constraint or more formally it is an unconstrained plane segment
tracking while introducing more planes and introduction of their inter-plane constraints could lead
to significant increase in stability and accuracy.
Figure 4: The result of the tracking when applied on a benchmarking image sequence with synthetic
transformations.
4.2 Real Sequence
The proposed tracking method is also tested on a real image benchmarking sequence that is
recorded by a downward looking camera mounted on a quadrocopter [30]. The sequence consists
of 633 images with the resolution 752x480 recorded by flying the quadrotor in a loop. The
important variable that was unavailable in case of real images is the absolute normal of the
tracked plane. There could be two ways to solve this problem: using an external plane detection
method to estimate the normal or using a rough estimate of the plane and leave rest to
optimization process. The former approach is more preferable and could lead to better
convergence rate however, to show the insensitivity of the proposed method to absolute plane
normal and depth estimates, we use the latter approach for evaluation. The rest of the
parameterization and initialization process is similar to simulated sequence based evaluation
process as described earlier.
It could be visualized in figure 6 that even though initial transformation of the marked region was
not correct and absolute normal was unknown; the tracking method learns the correct
transformation over a period of time and successfully tracks the planar region.
11. Computer Science & Information Technology (CS & IT) 289
A through error analysis is provided in the figure 7 which shows that the proposed tracking
method has good tracking ability with minimal translation and rotational error when the motion is
kept within the bounds of search space. A good way to keep the motion small is attained by using
high frame-rate cameras.
Figure 5: Convergence with variation in DOF and similarity measures. The columns represent similarity
measures SSD, NCC and MI respectively and rows represent DOF 2 and 4 respectively.
Figure 6: The result of the tracking when applied on a benchmarking image sequence with real
transformations.
12. 290 Computer Science & Information Technology (CS & IT)
Figure 7: Transformation error for image sequence.
4.3 Comparative analysis
The proposed method performs an optimization based tracking so comparative analyses with
variations of PSO and also with other state of the art methods help us determine its significance in
real applications. In figure 8 a comparison of the multiple variations of PSO is presented. The
Trelea PSO is good at converging to optimum similarity in all cases although its convergence rate
is not fastest due to being explorative in nature. PSO common on the other hand finds its way
quickly toward solution although it may not find global optimum due to being more exploitative
in nature. A group of three state of the art plane tracking methods (IC, FC and HA) are applied on
the same image sequence and a normalized root mean squared error is measured for the image
sequence and the number of iterations. As it can be visualized from the figure 9 that the algorithm
successfully beats IC and HA while it nears the performance of the forward compositional
approach.
Figure 8: comparison of the convergence rate of variations of PSO along with convergence behavior of best
performing version of PSO with different degree of freedom.
It can be noted that the IC and HA misses the track of the plane after 40th
iteration, most probably
due to intensity variation that is introduced in the sequence for which Taylor series approximation
failed to capture the intensity function. As a comparison if we check the performance of the
methods with different degree of freedom (see figure 10), we can see that the proposed PSO-
Track method performs decently.
13. Computer Science & Information Technology (CS & IT) 291
5. CONCLUSIONS
In this paper, we presented a novel approach toward solving camera tracking by tracking a
projection of the plane. A non-linear image alignment is adopted and correct parameters of the
transformation are recovered by optimizing the similarity between the planar regions. A through
comparative analysis of the method over simulated and real sequence of images reveal that the
proposed method has ability to track planar surfaces in when the motion within the frames is kept
small. The insensitivity of the method toward intensity variations as well as to unavailability of
true plane normal is also tested and algorithm has been found resilient to such environmental
changes.
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