The document describes a new algorithm for long-term robust visual tracking of moving objects in video sequences. The algorithm aims to overcome challenges such as geometric deformations, partial or total occlusions, and recovery after the target leaves the field of vision. It does not rely on a probabilistic process or require prior detection data. Experimental results on difficult video sequences demonstrate advantages over recent trackers. The algorithm can be used in applications like video surveillance, active vision, and industrial visual servoing.
Template matching is a basic method in image analysis to extract useful information from images. In this
paper, we suggest a new method for pattern matching. Our method transform the template image from two
dimensional image into one dimensional vector. Also all sub-windows (same size of template) in the
reference image will transform into one dimensional vectors. The three similarity measures SAD, SSD, and
Euclidean are used to compute the likeness between template and all sub-windows in the reference image
to find the best match. The experimental results show the superior performance of the proposed method
over the conventional methods on various template of different sizes.
Image segmentation by modified map ml estimationsijesajournal
Though numerous algorithms exist to perform image segmentation there are several issues
related to execution time of these algorithm. Image Segmentation is nothing but label relabeling
problem under probability framework. To estimate the label configuration, an iterative
optimization scheme is implemented to alternately carry out the maximum a posteriori (MAP)
estimation and the maximum likelihood (ML) estimations. In this paper this technique is
modified in such a way so that it performs segmentation within stipulated time period. The
extensive experiments shows that the results obtained are comparable with existing algorithms.
This algorithm performs faster execution than the existing algorithm to give automatic
segmentation without any human intervention. Its result match image edges very closer to
human perception.
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.
Title PDF: MATRIOSKA: A Multi-level Approach to Fast Tracking by Learning | ICIAP 2013
In this paper we propose a novel framework for the detection and tracking in real-time of unknown object in a video stream. We decompose the problem into two separate modules: detection and learning. The detection module can use multiple keypoint-based methods (ORB, FREAK, BRISK, SIFT, SURF and more) inside a fallback model, to correctly localize the object frame by frame exploiting the strengths of each method. The learning module updates the object model, with a growing and pruning approach, to account for changes in its appearance and extracts negative samples to further improve the detector performance. To show the effectiveness of the proposed tracking-by-detection algorithm, we present numerous quantitative results on a number of challenging sequences in which the target object goes through changes of pose, scale and illumination.
Geometric Correction for Braille Document Images csandit
Image processing is an important research area in computer vision. clustering is an unsupervised
study. clustering can also be used for image segmentation. there exist so many methods for image
segmentation. image segmentation plays an important role in image analysis.it is one of the first
and the most important tasks in image analysis and computer vision. this proposed system
presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering
algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy
significantly compared with classical fuzzy c-means algorithm. the new algorithm is called
gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of
gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and
image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity
area from the noisy images, using the clustering method, segmenting that portion separately using
content level set approach. the purpose of designing this system is to produce better segmentation
results for images corrupted by noise, so that it can be useful in various fields like medical image
analysis, such as tumor detection, study of anatomical structure, and treatment planning.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
Template matching is a basic method in image analysis to extract useful information from images. In this
paper, we suggest a new method for pattern matching. Our method transform the template image from two
dimensional image into one dimensional vector. Also all sub-windows (same size of template) in the
reference image will transform into one dimensional vectors. The three similarity measures SAD, SSD, and
Euclidean are used to compute the likeness between template and all sub-windows in the reference image
to find the best match. The experimental results show the superior performance of the proposed method
over the conventional methods on various template of different sizes.
Image segmentation by modified map ml estimationsijesajournal
Though numerous algorithms exist to perform image segmentation there are several issues
related to execution time of these algorithm. Image Segmentation is nothing but label relabeling
problem under probability framework. To estimate the label configuration, an iterative
optimization scheme is implemented to alternately carry out the maximum a posteriori (MAP)
estimation and the maximum likelihood (ML) estimations. In this paper this technique is
modified in such a way so that it performs segmentation within stipulated time period. The
extensive experiments shows that the results obtained are comparable with existing algorithms.
This algorithm performs faster execution than the existing algorithm to give automatic
segmentation without any human intervention. Its result match image edges very closer to
human perception.
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.
Title PDF: MATRIOSKA: A Multi-level Approach to Fast Tracking by Learning | ICIAP 2013
In this paper we propose a novel framework for the detection and tracking in real-time of unknown object in a video stream. We decompose the problem into two separate modules: detection and learning. The detection module can use multiple keypoint-based methods (ORB, FREAK, BRISK, SIFT, SURF and more) inside a fallback model, to correctly localize the object frame by frame exploiting the strengths of each method. The learning module updates the object model, with a growing and pruning approach, to account for changes in its appearance and extracts negative samples to further improve the detector performance. To show the effectiveness of the proposed tracking-by-detection algorithm, we present numerous quantitative results on a number of challenging sequences in which the target object goes through changes of pose, scale and illumination.
Geometric Correction for Braille Document Images csandit
Image processing is an important research area in computer vision. clustering is an unsupervised
study. clustering can also be used for image segmentation. there exist so many methods for image
segmentation. image segmentation plays an important role in image analysis.it is one of the first
and the most important tasks in image analysis and computer vision. this proposed system
presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering
algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy
significantly compared with classical fuzzy c-means algorithm. the new algorithm is called
gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of
gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and
image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity
area from the noisy images, using the clustering method, segmenting that portion separately using
content level set approach. the purpose of designing this system is to produce better segmentation
results for images corrupted by noise, so that it can be useful in various fields like medical image
analysis, such as tumor detection, study of anatomical structure, and treatment planning.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
A Review on Classification Based Approaches for STEGanalysis DetectionEditor IJCATR
This document summarizes two approaches for image steganalysis detection. The first approach proposes novel steganalysis algorithms based on how data hiding affects the rate-distortion characteristics of images. Features are extracted based on increased image entropy and small, imperceptible distortions from data embedding. A Bayesian classifier is then trained on these features. The second approach uses contourlet transform to represent images. It extracts features based on the first four normalized statistical moments of high and low frequency subbands and structural similarity measure of medium frequency subbands. A non-linear support vector machine is then used for classification. Experimental results show the proposed approaches can efficiently detect stego images with high accuracy and low computational cost.
A novel method is proposed for image segmentation based on probabilistic field theory. This model assumes that the whole pixels of an image and some unknown parameters form a field. According to this model, the pixel labels are generated by a compound function of the field. The main novelty of this model is it consider the features of the pixels and the interdependent among the pixels. The parameters are generated by a novel spatially variant mixture model and estimated by expectation-maximization (EM)-
based algorithm. Thus, we simultaneously impose the spatial smoothness on the prior knowledge. Numerical experiments are presented where the proposed method and other mixture model-based methods were tested on synthetic and real world images. These experimental results demonstrate that our algorithm achieves competitive performance compared to other methods.
Optimization of Macro Block Size for Adaptive Rood Pattern Search Block Match...IJERA Editor
In area of video compression, Motion Estimation is one of the most important modules and play an important role
to design and implementation of any the video encoder. It consumes more than 85% of video encoding time due to
searching of a candidate block in the search window of the reference frame. Various block matching methods have
been developed to minimize the search time. In this context, Adaptive Rood Pattern Search is one of the less
expensive block matching methods, which is widely acceptable for better Motion Estimation in video data
processing. In this paper we have proposed to optimize the macro block size used in adaptive rood pattern search
method for improvement in motion estimation.
An adaptive gmm approach to background subtraction for application in real ti...eSAT Publishing House
This document presents an adaptive Gaussian mixture model (AGMM) approach for background subtraction and shadow detection in real-time video surveillance applications. The AGMM method models each pixel as a mixture of Gaussians and can adapt to changing lighting and repetitive motions. Shadows are detected using the Horpresert color model, which measures brightness and chromaticity distortions to identify shadow pixels. Test results show the AGMM approach can achieve real-time background subtraction at 20 frames per second and effectively detect shadows. The proposed method does not require pre-defined background models and can operate adaptively in different environments and illumination conditions, making it suitable for security and surveillance applications.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Development of stereo matching algorithm based on sum of absolute RGB color d...IJECEIAES
This article presents local-based stereo matching algorithm which comprises a devel- opment of an algorithm using block matching and two edge preserving filters in the framework. Fundamentally, the matching process consists of several stages which will produce the disparity or depth map. The problem and most challenging work for matching process is to get an accurate corresponding point between two images. Hence, this article proposes an algorithm for stereo matching using improved Sum of Absolute RGB Differences (SAD), gradient matching and edge preserving filters. It is Bilateral Filter (BF) to surge up the accuracy. The SAD and gradient matching will be implemented at the first stage to get the preliminary corresponding result, then the BF works as an edge-preserving filter to remove the noise from the first stage. The second BF is used at the last stage to improve final disparity map and increase the object boundaries. The experimental analysis and validation are using the Middlebury standard benchmarking evaluation system. Based on the results, the proposed work is capable to increase the accuracy and to preserve the object edges. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it shows the proposed work in this article perform much better.
The document presents a new method for segmenting MR brain images that combines a hidden Markov random field (HMRF) model with a hybrid metaheuristic optimization algorithm. The HMRF model uses adaptive parameters to balance contributions from different tissue classes during segmentation. The hybrid metaheuristic algorithm improves the quality of solutions during HMRF optimization by combining the cuckoo search and particle swarm optimization algorithms. Experimental results on simulated and real MR brain images show the proposed method achieves satisfactory segmentation performance for images with noise and intensity inhomogeneity.
Kandemir Inferring Object Relevance From Gaze In Dynamic ScenesKalle
As prototypes of data glasses having both data augmentation and gaze tracking capabilities are becoming available, it is now possible to develop proactive gaze-controlled user interfaces to display information about objects, people, and other entities in real-world setups. In order to decide which objects the augmented information should be about, and how saliently to augment, the system needs an estimate of the importance or relevance of the objects of the scene for the user at a given time. The estimates will be used to minimize distraction of the user, and for providing efficient spatial management of the augmented items. This work is a feasibility study on inferring the relevance of objects in dynamic scenes from gaze. We collected gaze data from subjects watching a video for a pre-defined task. The results show that a simple ordinal logistic regression model gives relevance rankings of scene objects with a promising accuracy.
Disparity Estimation by a Real Time Approximation AlgorithmCSCJournals
This document summarizes an approximation algorithm for real-time disparity estimation of stereo images. The algorithm shrinks the left and right images 3 times to reduce computational time and search area. Disparity is estimated from the shrunk images and then extrapolated to reconstruct the original disparity image. Experimental results on standard stereo images show the algorithm reduces computational time by 76.34% compared to traditional window-based methods, with acceptable accuracy. Some accuracy is lost due to pixel quantization during shrinking and extrapolation, but the fast estimation of dense disparity makes the algorithm useful for applications requiring real-time performance.
This document presents a new approach called Pillar-K-means for image segmentation. Pillar-K-means applies a clustering algorithm called K-means to group pixels in images, but first optimizes the K-means process using an algorithm called Pillar. This is done to improve precision and reduce computation time for image segmentation. The document describes K-means clustering and its issues, then introduces Pillar-K-means which optimizes K-means initialization to enhance segmentation accuracy and speed. Experiments show Pillar-K-means improved over standard K-means.
This document discusses using artificial neural networks for network intrusion detection. Specifically, it proposes a hybrid classification model that uses entropy-based feature selection to reduce the dataset, followed by four neural network techniques (RBFN, SOM, SMO, PART) for classification. It provides details on each neural network technique and the overall methodology, which uses 10-fold cross validation to evaluate performance based on standard criteria. The goal is to build an efficient intrusion detection system with low false alarms and high detection rates.
This document summarizes a research paper on using a k-means clustering method to detect brain tumors in MRI images. The paper introduces brain tumors and MRI imaging. It then describes using k-means clustering for tumor segmentation, which groups similar image patterns into clusters to identify the tumor region. The paper presents results of applying k-means to two MRI images, including statistical measures of segmentation accuracy, tumor area comparison, and timing. The k-means method achieved average rand index of 0.8358, low average errors, and tumor areas close to manual segmentation in under 3 seconds, demonstrating potential for accurate and efficient brain tumor detection.
Graph Theory Based Approach For Image Segmentation Using Wavelet TransformCSCJournals
This paper presents the image segmentation approach based on graph theory and threshold. Amongst the various segmentation approaches, the graph theoretic approaches in image segmentation make the formulation of the problem more flexible and the computation more resourceful. The problem is modeled in terms of partitioning a graph into several sub-graphs; such that each of them represents a meaningful region in the image. The segmentation problem is then solved in a spatially discrete space by the well-organized tools from graph theory. After the literature review, the problem is formulated regarding graph representation of image and threshold function. The boundaries between the regions are determined as per the segmentation criteria and the segmented regions are labeled with random colors. In presented approach, the image is preprocessed by discrete wavelet transform and coherence filter before graph segmentation. The experiments are carried out on a number of natural images taken from Berkeley Image Database as well as synthetic images from online resources. The experiments are performed by using the wavelets of Haar, DB2, DB4, DB6 and DB8. The results are evaluated and compared by using the performance evaluation parameters like execution time, Performance Ratio, Peak Signal to Noise Ratio, Precision and Recall and obtained results are encouraging.
Image fusion using nsct denoising and target extraction for visual surveillanceeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The document describes a method for tracking objects of deformable shapes in images. It proposes representing the matching of a deformable template to an image as a minimum cost cyclic path in a product space of the template and image. An energy functional is introduced that consists of a data term favoring strong image gradients, a shape consistency term favoring similar tangent angles, and an elastic penalty. Optimization is performed using a minimum ratio cycle algorithm parallelized on GPUs. This provides efficient, pixel-accurate segmentation and correspondence between template and image curve. The method can be extended to 4D to segment and track multiple deformable anatomical structures in medical images.
MULTIPLE HUMAN TRACKING USING RETINANET FEATURES, SIAMESE NEURAL NETWORK, AND...IAEME Publication
Multiple human tracking based on object detection has been a challenge due to its
complexity. Errors in object detection would be propagated to tracking errors. In this
paper, we propose a tracking method that minimizes the error produced by object
detector. We use RetinaNet as object detector and Hungarian algorithm for tracking.
The cost matrix for Hungarian algorithm is calculated using the RetinaNet features,
bounding box center distances, and intersection of unions of bounding boxes. We
interpolate the missing detections in the last step. The proposed method yield 43.2
MOTA for MOT16 benchmark
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.
This document discusses using particle swarm optimization (PSO) to design optimal close-range photogrammetry networks. PSO is introduced as a heuristic optimization algorithm inspired by bird flocking behavior that can be used to solve complex optimization problems. The document then provides an overview of close-range photogrammetry network design and the four design stages. It explains that PSO will be used to optimize the first stage of determining optimal camera station positions. Mathematical models of PSO for close-range photogrammetry network design are developed. Experimental tests are carried out to develop a PSO algorithm that can determine optimum camera positions and evaluate the accuracy of the developed network.
Stereo matching based on absolute differences for multiple objects detectionTELKOMNIKA JOURNAL
This article presents a new algorithm for object detection using stereo camera system. The problem to get an accurate object detion using stereo camera is the imprecise of matching process between two scenes with the same viewpoint. Hence, this article aims to reduce the incorrect matching pixel with four stages. This new algorithm is the combination of continuous process of matching cost computation, aggregation, optimization and filtering. The first stage is matching cost computation to acquire preliminary result using an absolute differences method. Then the second stage known as aggregation step uses a guided filter with fixed window support size. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter. It is effectively further decrease the error on the disparity map which contains information of object detection and locations. The proposed work produces low errors (i.e., 12.11% and 14.01% nonocc and all errors) based on the KITTI dataset and capable to perform much better compared with before the proposed framework and competitive with some newly available methods.
A Survey On Tracking Moving Objects Using Various AlgorithmsIJMTST Journal
Sparse representation has been applied to the object tracking problem. Mining the self- similarities between particles via multitask learning can improve tracking performance. How-ever, some particles may be different from others when they are sampled from a large region. Imposing all particles share the same structure may degrade the results. To overcome this problem, we propose a tracking algorithm based on robust multitask sparse representation (RMTT) in this letter. When we learn the particle representations, we decompose the sparse coefficient matrix into two parts in our algorithm. Joint sparse regularization is imposed on one coefficient matrix while element-wise sparse regularization is imposed on another matrix. The former regularization exploits self-similarities of particles while the later one considers the differences between them.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
This document discusses multidimensional clustering methods for data mining and their industrial applications. It begins with an introduction to clustering, including definitions and goals. Popular clustering algorithms are described, such as K-means, fuzzy C-means, hierarchical clustering, and mixture of Gaussians. Distance measures and their importance in clustering are covered. The K-means and fuzzy C-means algorithms are explained in detail. Examples are provided to illustrate fuzzy C-means clustering. Finally, applications of clustering algorithms in fields such as marketing, biology, and earth sciences are mentioned.
This document summarizes a research paper on background subtraction techniques for motion detection in video. It describes a proposed technique that stores and compares past pixel values to the current value to determine if a pixel belongs to the background or foreground. It also discusses using a k-means algorithm and Gaussian mixture model to build a probabilistic background model and classify pixels. The paper evaluates different shadow detection approaches and finds RGB color spaces perform best for segmentation and shadow removal.
A Review on Classification Based Approaches for STEGanalysis DetectionEditor IJCATR
This document summarizes two approaches for image steganalysis detection. The first approach proposes novel steganalysis algorithms based on how data hiding affects the rate-distortion characteristics of images. Features are extracted based on increased image entropy and small, imperceptible distortions from data embedding. A Bayesian classifier is then trained on these features. The second approach uses contourlet transform to represent images. It extracts features based on the first four normalized statistical moments of high and low frequency subbands and structural similarity measure of medium frequency subbands. A non-linear support vector machine is then used for classification. Experimental results show the proposed approaches can efficiently detect stego images with high accuracy and low computational cost.
A novel method is proposed for image segmentation based on probabilistic field theory. This model assumes that the whole pixels of an image and some unknown parameters form a field. According to this model, the pixel labels are generated by a compound function of the field. The main novelty of this model is it consider the features of the pixels and the interdependent among the pixels. The parameters are generated by a novel spatially variant mixture model and estimated by expectation-maximization (EM)-
based algorithm. Thus, we simultaneously impose the spatial smoothness on the prior knowledge. Numerical experiments are presented where the proposed method and other mixture model-based methods were tested on synthetic and real world images. These experimental results demonstrate that our algorithm achieves competitive performance compared to other methods.
Optimization of Macro Block Size for Adaptive Rood Pattern Search Block Match...IJERA Editor
In area of video compression, Motion Estimation is one of the most important modules and play an important role
to design and implementation of any the video encoder. It consumes more than 85% of video encoding time due to
searching of a candidate block in the search window of the reference frame. Various block matching methods have
been developed to minimize the search time. In this context, Adaptive Rood Pattern Search is one of the less
expensive block matching methods, which is widely acceptable for better Motion Estimation in video data
processing. In this paper we have proposed to optimize the macro block size used in adaptive rood pattern search
method for improvement in motion estimation.
An adaptive gmm approach to background subtraction for application in real ti...eSAT Publishing House
This document presents an adaptive Gaussian mixture model (AGMM) approach for background subtraction and shadow detection in real-time video surveillance applications. The AGMM method models each pixel as a mixture of Gaussians and can adapt to changing lighting and repetitive motions. Shadows are detected using the Horpresert color model, which measures brightness and chromaticity distortions to identify shadow pixels. Test results show the AGMM approach can achieve real-time background subtraction at 20 frames per second and effectively detect shadows. The proposed method does not require pre-defined background models and can operate adaptively in different environments and illumination conditions, making it suitable for security and surveillance applications.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Development of stereo matching algorithm based on sum of absolute RGB color d...IJECEIAES
This article presents local-based stereo matching algorithm which comprises a devel- opment of an algorithm using block matching and two edge preserving filters in the framework. Fundamentally, the matching process consists of several stages which will produce the disparity or depth map. The problem and most challenging work for matching process is to get an accurate corresponding point between two images. Hence, this article proposes an algorithm for stereo matching using improved Sum of Absolute RGB Differences (SAD), gradient matching and edge preserving filters. It is Bilateral Filter (BF) to surge up the accuracy. The SAD and gradient matching will be implemented at the first stage to get the preliminary corresponding result, then the BF works as an edge-preserving filter to remove the noise from the first stage. The second BF is used at the last stage to improve final disparity map and increase the object boundaries. The experimental analysis and validation are using the Middlebury standard benchmarking evaluation system. Based on the results, the proposed work is capable to increase the accuracy and to preserve the object edges. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it shows the proposed work in this article perform much better.
The document presents a new method for segmenting MR brain images that combines a hidden Markov random field (HMRF) model with a hybrid metaheuristic optimization algorithm. The HMRF model uses adaptive parameters to balance contributions from different tissue classes during segmentation. The hybrid metaheuristic algorithm improves the quality of solutions during HMRF optimization by combining the cuckoo search and particle swarm optimization algorithms. Experimental results on simulated and real MR brain images show the proposed method achieves satisfactory segmentation performance for images with noise and intensity inhomogeneity.
Kandemir Inferring Object Relevance From Gaze In Dynamic ScenesKalle
As prototypes of data glasses having both data augmentation and gaze tracking capabilities are becoming available, it is now possible to develop proactive gaze-controlled user interfaces to display information about objects, people, and other entities in real-world setups. In order to decide which objects the augmented information should be about, and how saliently to augment, the system needs an estimate of the importance or relevance of the objects of the scene for the user at a given time. The estimates will be used to minimize distraction of the user, and for providing efficient spatial management of the augmented items. This work is a feasibility study on inferring the relevance of objects in dynamic scenes from gaze. We collected gaze data from subjects watching a video for a pre-defined task. The results show that a simple ordinal logistic regression model gives relevance rankings of scene objects with a promising accuracy.
Disparity Estimation by a Real Time Approximation AlgorithmCSCJournals
This document summarizes an approximation algorithm for real-time disparity estimation of stereo images. The algorithm shrinks the left and right images 3 times to reduce computational time and search area. Disparity is estimated from the shrunk images and then extrapolated to reconstruct the original disparity image. Experimental results on standard stereo images show the algorithm reduces computational time by 76.34% compared to traditional window-based methods, with acceptable accuracy. Some accuracy is lost due to pixel quantization during shrinking and extrapolation, but the fast estimation of dense disparity makes the algorithm useful for applications requiring real-time performance.
This document presents a new approach called Pillar-K-means for image segmentation. Pillar-K-means applies a clustering algorithm called K-means to group pixels in images, but first optimizes the K-means process using an algorithm called Pillar. This is done to improve precision and reduce computation time for image segmentation. The document describes K-means clustering and its issues, then introduces Pillar-K-means which optimizes K-means initialization to enhance segmentation accuracy and speed. Experiments show Pillar-K-means improved over standard K-means.
This document discusses using artificial neural networks for network intrusion detection. Specifically, it proposes a hybrid classification model that uses entropy-based feature selection to reduce the dataset, followed by four neural network techniques (RBFN, SOM, SMO, PART) for classification. It provides details on each neural network technique and the overall methodology, which uses 10-fold cross validation to evaluate performance based on standard criteria. The goal is to build an efficient intrusion detection system with low false alarms and high detection rates.
This document summarizes a research paper on using a k-means clustering method to detect brain tumors in MRI images. The paper introduces brain tumors and MRI imaging. It then describes using k-means clustering for tumor segmentation, which groups similar image patterns into clusters to identify the tumor region. The paper presents results of applying k-means to two MRI images, including statistical measures of segmentation accuracy, tumor area comparison, and timing. The k-means method achieved average rand index of 0.8358, low average errors, and tumor areas close to manual segmentation in under 3 seconds, demonstrating potential for accurate and efficient brain tumor detection.
Graph Theory Based Approach For Image Segmentation Using Wavelet TransformCSCJournals
This paper presents the image segmentation approach based on graph theory and threshold. Amongst the various segmentation approaches, the graph theoretic approaches in image segmentation make the formulation of the problem more flexible and the computation more resourceful. The problem is modeled in terms of partitioning a graph into several sub-graphs; such that each of them represents a meaningful region in the image. The segmentation problem is then solved in a spatially discrete space by the well-organized tools from graph theory. After the literature review, the problem is formulated regarding graph representation of image and threshold function. The boundaries between the regions are determined as per the segmentation criteria and the segmented regions are labeled with random colors. In presented approach, the image is preprocessed by discrete wavelet transform and coherence filter before graph segmentation. The experiments are carried out on a number of natural images taken from Berkeley Image Database as well as synthetic images from online resources. The experiments are performed by using the wavelets of Haar, DB2, DB4, DB6 and DB8. The results are evaluated and compared by using the performance evaluation parameters like execution time, Performance Ratio, Peak Signal to Noise Ratio, Precision and Recall and obtained results are encouraging.
Image fusion using nsct denoising and target extraction for visual surveillanceeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The document describes a method for tracking objects of deformable shapes in images. It proposes representing the matching of a deformable template to an image as a minimum cost cyclic path in a product space of the template and image. An energy functional is introduced that consists of a data term favoring strong image gradients, a shape consistency term favoring similar tangent angles, and an elastic penalty. Optimization is performed using a minimum ratio cycle algorithm parallelized on GPUs. This provides efficient, pixel-accurate segmentation and correspondence between template and image curve. The method can be extended to 4D to segment and track multiple deformable anatomical structures in medical images.
MULTIPLE HUMAN TRACKING USING RETINANET FEATURES, SIAMESE NEURAL NETWORK, AND...IAEME Publication
Multiple human tracking based on object detection has been a challenge due to its
complexity. Errors in object detection would be propagated to tracking errors. In this
paper, we propose a tracking method that minimizes the error produced by object
detector. We use RetinaNet as object detector and Hungarian algorithm for tracking.
The cost matrix for Hungarian algorithm is calculated using the RetinaNet features,
bounding box center distances, and intersection of unions of bounding boxes. We
interpolate the missing detections in the last step. The proposed method yield 43.2
MOTA for MOT16 benchmark
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.
This document discusses using particle swarm optimization (PSO) to design optimal close-range photogrammetry networks. PSO is introduced as a heuristic optimization algorithm inspired by bird flocking behavior that can be used to solve complex optimization problems. The document then provides an overview of close-range photogrammetry network design and the four design stages. It explains that PSO will be used to optimize the first stage of determining optimal camera station positions. Mathematical models of PSO for close-range photogrammetry network design are developed. Experimental tests are carried out to develop a PSO algorithm that can determine optimum camera positions and evaluate the accuracy of the developed network.
Stereo matching based on absolute differences for multiple objects detectionTELKOMNIKA JOURNAL
This article presents a new algorithm for object detection using stereo camera system. The problem to get an accurate object detion using stereo camera is the imprecise of matching process between two scenes with the same viewpoint. Hence, this article aims to reduce the incorrect matching pixel with four stages. This new algorithm is the combination of continuous process of matching cost computation, aggregation, optimization and filtering. The first stage is matching cost computation to acquire preliminary result using an absolute differences method. Then the second stage known as aggregation step uses a guided filter with fixed window support size. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter. It is effectively further decrease the error on the disparity map which contains information of object detection and locations. The proposed work produces low errors (i.e., 12.11% and 14.01% nonocc and all errors) based on the KITTI dataset and capable to perform much better compared with before the proposed framework and competitive with some newly available methods.
A Survey On Tracking Moving Objects Using Various AlgorithmsIJMTST Journal
Sparse representation has been applied to the object tracking problem. Mining the self- similarities between particles via multitask learning can improve tracking performance. How-ever, some particles may be different from others when they are sampled from a large region. Imposing all particles share the same structure may degrade the results. To overcome this problem, we propose a tracking algorithm based on robust multitask sparse representation (RMTT) in this letter. When we learn the particle representations, we decompose the sparse coefficient matrix into two parts in our algorithm. Joint sparse regularization is imposed on one coefficient matrix while element-wise sparse regularization is imposed on another matrix. The former regularization exploits self-similarities of particles while the later one considers the differences between them.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
This document discusses multidimensional clustering methods for data mining and their industrial applications. It begins with an introduction to clustering, including definitions and goals. Popular clustering algorithms are described, such as K-means, fuzzy C-means, hierarchical clustering, and mixture of Gaussians. Distance measures and their importance in clustering are covered. The K-means and fuzzy C-means algorithms are explained in detail. Examples are provided to illustrate fuzzy C-means clustering. Finally, applications of clustering algorithms in fields such as marketing, biology, and earth sciences are mentioned.
This document summarizes a research paper on background subtraction techniques for motion detection in video. It describes a proposed technique that stores and compares past pixel values to the current value to determine if a pixel belongs to the background or foreground. It also discusses using a k-means algorithm and Gaussian mixture model to build a probabilistic background model and classify pixels. The paper evaluates different shadow detection approaches and finds RGB color spaces perform best for segmentation and shadow removal.
This document summarizes a research paper on background subtraction techniques for motion detection in video. It describes a proposed technique that stores and compares past pixel values to the current value to determine if a pixel belongs to the background or foreground. It also discusses using a k-means algorithm and Gaussian mixture model to build a probabilistic background model and classify pixels. The paper evaluates different shadow detection approaches and finds RGB color spaces perform best for segmentation and shadow removal.
Schematic model for analyzing mobility and detection of multipleIAEME Publication
The document discusses a schematic model for analyzing mobility and detecting multiple objects in traffic scenes. It aims to not only detect and count moving objects, but also understand crowd behavior and reduce issues with objects occluding each other. Previous work on object detection is reviewed, noting that most approaches do not integrate detecting multiple objects simultaneously or address problems of object occlusion. The proposed model uses background subtraction and unscented Kalman filtering to increase detection accuracy and reduce false positives when analyzing image sequences of traffic scenes to detect multiple moving objects. It was tested in MATLAB and results showed highly accurate detection rates.
Implementation of Object Tracking for Real Time VideoIDES Editor
Real-time tracking of object boundaries is an
important task in many vision applications. Here we propose
an approach to implement the level set method. This approach
does not need to solve any partial differential equations (PDFs),
thus reducing the computation dramatically compared with
optimized narrow band techniques proposed before. With our
approach, real-time level-set based video tracking can be
achieved.
Efficient 3D stereo vision stabilization for multi-camera viewpointsjournalBEEI
In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing
the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method.
Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds.
This document discusses approaches for video segmentation. It describes tracking particles across frames to identify motion patterns, then clustering the particles to obtain a pixel-wise segmentation over space and time. This addresses limitations of segmentation based on motion boundaries. Reality-based 3D models can help address complex spatial motions by representing objects and their relationships in 3D space. The document also reviews direct and feature-based motion estimation methods, variational and level-set segmentation frameworks, and challenges including fitting motion models to data and handling outliers.
This document summarizes a method for tracking deformable objects in images. It proposes casting the problem as finding optimal cyclic paths in a product space of the template shape and input image. A cost functional is introduced that considers data fidelity, shape consistency, and elastic deformation. The functional is optimized using a minimum ratio cycle algorithm on graphics cards, allowing real-time segmentation and tracking of deformable objects while guaranteeing a globally optimal solution. The method can be extended to track multiple deformable anatomical structures in medical images.
This document summarizes a method for tracking deformable objects in images. It proposes casting the problem as finding optimal cyclic paths in a product space of the template shape and input image. A cost functional is introduced that consists of three terms: data fidelity favoring strong edges, shape consistency favoring similar tangent angles, and an elastic penalty for stretching. Optimization is performed using simulated annealing for segmentation and iterated conditional modes for tracking. The algorithm provides optimal segmentation and point correspondences between template and image curve in linear time.
This document summarizes a research paper that proposes a new approach for tracking multiple deformable anatomical structures in medical images using geometrically deformable templates (GDTs). The GDTs can deform to match similar shapes based on image forces while minimizing a penalty function that measures deformation from the template's equilibrium shape. This allows simultaneous segmentation of multiple objects using intra- and inter-shape information. Simulated annealing is used for segmentation while iterated conditional modes is used for tracking. The paper also reviews previous work on image segmentation, tracking deformable objects, and shape-based image segmentation.
Super-resolution (SR) is the process of obtaining a high resolution (HR) image or
a sequence of HR images from a set of low resolution (LR) observations. The block
matching algorithms used for motion estimation to obtain motion vectors between the
frames in Super-resolution. The implementation and comparison of two different types of
block matching algorithms viz. Exhaustive Search (ES) and Spiral Search (SS) are
discussed. Advantages of each algorithm are given in terms of motion estimation
computational complexity and Peak Signal to Noise Ratio (PSNR). The Spiral Search
algorithm achieves PSNR close to that of Exhaustive Search at less computation time than
that of Exhaustive Search. The algorithms that are evaluated in this paper are widely used
in video super-resolution and also have been used in implementing various video standards
like H.263, MPEG4, H.264.
This document presents a methodology for real-time object tracking using a webcam. It combines Prewitt edge detection for object detection and Kalman filtering for tracking. Prewitt edge detection is used to detect the edges of the moving object in each video frame. Then, Kalman filtering is used to track the detected object across subsequent frames by predicting its location. Experiments show the approach can efficiently track objects under deformation, occlusion, and can track multiple objects simultaneously. The combination of Prewitt edge detection and Kalman filtering provides an effective method for real-time object tracking.
Particle Swarm Optimization for Nano-Particles Extraction from Supporting Mat...CSCJournals
This document summarizes a study that uses Particle Swarm Optimization (PSO) for automatic segmentation of nano-particles in Transmission Electron Microscopy (TEM) images. PSO is applied to specify local and global thresholds for segmentation by treating image entropy as a minimization problem. Results show the PSO method improves over previous techniques by reducing incorrect characterization of nano-particles in images affected by liquid concentrations or supporting materials, with up to a 27% reduction in errors. Compared to manual characterization, PSO provides comparable particle counting with higher computational efficiency suitable for real-time analysis.
IRJET - A Systematic Observation in Digital Image Forgery Detection using MATLABIRJET Journal
This document summarizes a research paper that proposes a new method for detecting digital image forgeries using analysis of illumination inconsistencies. The method extracts texture and edge-based features from illuminant maps of face regions in an image. These features are then classified using machine learning to detect if faces are illuminated inconsistently, indicating tampering. The approach requires only minimal user interaction by specifying bounding boxes around faces. Evaluation shows the method achieves a 86% detection rate of spliced images, outperforming existing illumination-based approaches. The work presents an important step in reducing human interaction for illumination-based forgery detection.
MAGNETIC RESONANCE BRAIN IMAGE SEGMENTATIONVLSICS Design
Segmentation of tissues and structures from medical images is the first step in many image analysis applications developed for medical diagnosis. With the growing research on medical image segmentation, it is essential to categorize the research outcomes and provide researchers with an overview of the existing segmentation techniques in medical images. In this paper, different image segmentation methods applied on magnetic resonance brain images are reviewed. The selection of methods includes sources from image processing journals, conferences, books, dissertations and thesis. The conceptual details of the methods are explained and mathematical details are avoided for simplicity. Both broad and detailed categorizations of reviewed segmentation techniques are provided. The state of art research is provided with emphasis on developed techniques and image properties used by them. The methods defined are not always mutually independent. Hence, their inter relationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application.
Similar to Long-Term Robust Tracking Whith on Failure Recovery (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Mechatronics is a multidisciplinary field that refers to the skill sets needed in the contemporary, advanced automated manufacturing industry. At the intersection of mechanics, electronics, and computing, mechatronics specialists create simpler, smarter systems. Mechatronics is an essential foundation for the expected growth in automation and manufacturing.
Mechatronics deals with robotics, control systems, and electro-mechanical systems.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
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confidence map is analyzed to estimate the most likely location of the object. Finally, the
classifier is updated in an unsupervised manner using randomly selected patches by [2].
Another approach [3] is to locate precisely the target object at each image in order to
prevent tracking errors. Based on a structured SVM framework, it addresses the limitation of
previous trackers, such as [4, 5], which separate the target location (Samples Labeling in a
Research Region) and updating the model in two separate steps. This dichotomy introduces
additional labeling errors to the Update Model because the sample chosen by the classifier may
not correspond to the best-estimated object location.
The optical flow approach improves motion monitoring algorithm of several objects with
a reformed location by [6]. Another improvement is a long-lasting algorithm, which allows a
tracking with panne recovery. After choosing a tracked object in the first frame forward and
backward in time, they calculate the distance between these two trajectories. If the distance is
greater than the threshold, tracking is likely to fail, however the return of the most recent object
model by the detector will reset the tracker. The major problem of the optical flow approach
always remains the change of illumination [7]. An algorithm for object traking via prototypes is
presented in [8]. The author of [9] present a robust visual tracking with an improved subspace
representation model. In [10] the authors discuss an algorithm that makes product rule and
weighted sum rule unified into an adaptive framework according to defined features distance.
In [11] the authors introduce an algorithm of hybrid tracking through the analysis and
experiments associated with the software of sporting video. A real time tracking algorithm based
on particle filter with gaussian weighting is presented in [12]. We propose in this work a versatile
and generic system of perception (NSSD_DT) based on an active perception strategy (synoptic
diagram Figure 01).
Figure 1. Synoptic Diagram
First, we studied measuring techniques of difference and similarity (SAD, SSD, NSSD,
CC, NCC) for matching method on an evaluation bench to choose the most effective in terms of
recognition. The study allowed the selection of the normalized square difference function
(NSSD).
Then we have dealt with the problem of tracking visual objects which aims to locate a
target over time, in particular, we have focused on the difficult scenarios in which the object
undergoes important deformations and occlusions, or leaves the field of vision. To achieve this
objective, we proposed a robust method based on a similarity sentinel index to update the
template when it reaches it. Our tracker has the performance to detect tracking failure and
recover after failure. We therefore solve all deformation and occlusion problems to ensure a
robust tracking in the long term.
2. Matching Method
The matching model is adopted to detect small parts that corresponds to a template
image. This technique is widely used in object detection fields such as vehicle tracking, robotics,
medical imaging and in the industry as part of quality control.
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The crucial point is to adopt an appropriate "measure" to quantify similarity or matching.
However, this method also requires a high computational cost since the matching process
involves moving the model image to all possible positions in a larger source image and
calculating a numerical index indicating how much the pattern corresponds to the image in that
position. This problem is therefore considered as an optimization problem.
The measurement of the correspondence between two images is considered as a
metric that indicates the degree of resemblance or dissimilarity between them. This metric may
be increasing or decreasing with a degree of similarity. When the metric is specifically indicated
as a measure of inadequacy, it is an amount that increases with the degree of dissimilarity.
By sliding, we move the patch one pixel at a time (left to right, up to down). At each
location, a metric is calculated, it represents “good” or “bad” match at that location. For each
location of Template over source image, we store the metric in the result matrix (R). Each
location (xy) in R contains the match metric. The image below is the result R of sliding the patch
with a metric NSSD. The brightest locations indicate the highest matches. The location marked
by the red circle is the one with the highest value. Thus, that location (the rectangle formed by
that point as a corner and width and height equal to the patch image) is considered the match.
Figure 2. Result matrix (R)
3. Techniques Related to Matching Model
In matching, the source object can be turned, occluded or set to another scale.
Techniques that provide for a distinct model for each scale and orientation, round rigid models.
Though, they are too expensive, especially for large Models. The idea is to be robust and fill as
much as possible all these deficiencies, with more flexibility and with an optimized cost.
The proposed approach begins with a metric study of two methods, Normalized
Correlation and Normalized Square Difference. Afterwards, we propose an improvement of a
selected method on the aforementioned preference criterion.
3.1. Normalized Cross Correlation Method and Normalized Square Difference Method
3.1.1. Normalized Cross Correlation Method
The cross-correlation function is an operator that acts on two functions (f (x, y), g (x, y)),
each corresponding to an image. This operator has the property of 1 when the two functions are
identical and of tending towards -1 when the functions are different. In 2D, to measure the
relative displacement of two images along image x and y axes, a correlation algorithm (1), (2)
uses this operator, taking as functions f and g respectively portions of the reference and
deformed images. The algorithm searches for the values of displacements dx and dy such that
g (x + dx, y + dy) maximizes the correlation operator with f. These values are retained as the
best displacement estimation of the image g with respect to the image f.
∑ ( ) ( )
√∑ ∑
(1)
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In practice, the algorithm applies this procedure to image series, which are parts of the
reference image. It calculates the correlation function between a reference image and a
distorted image. The deformed image giving the greatest cross-correlation function with the
reference image is retained as the best and thus makes it possible to estimate the displacement
at this stage.
Figure 3. Correspondence values for normalized correlation method
For values varying between 0.999 and 0.930, a correspondence of 44% is obtained.
3.1.2. Normalized Square Sum Difference Method (NSSD)
∑ ∑ [ ( ) ( )]
√∑ ∑ ∑ ∑
(2)
Figure 4. Correspondence values of the sum square difference normalized method
For values varying between 0.999 and 0.430, a good correspondence of 98% hence better
matching resistance.
3.1.3 Comparison of matching methods
The normalized cross-correlation method, comparing concordance values to zero (ideal
values), for correlation methods, normalized correlations, normalized difference and normalized
square difference, compare the matching values to 1 (ideal values) [13].
After testing the six methods, the best results are given by the normalized correlation
and the normalized square difference method, above the performance evaluation curves for
both methods. For the two selected methods, by fixing the model and modifying the source, we
obtain the curves in Figure 3 and 4 of the variations of the correspondence value according to
the variation of the source.
Sum Square Difference Normalized Curve
Corresp
ondence
Values
Images
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In similarity measurements, the NSSD method has less computational cost since it is
only a square operation and a subtraction of pixels between the model and the original image.
In addition, it takes less time to search the area in an image corresponding to the model. On the
other hand, NCC is better than the SSD because it involves a multiplication, a division and a
square root operation.
3.2. Basic algorithms: NSSD
The algorithm below represents the matching based on the normalized squared
difference method.
The Normalized sum squared difference algorithm
1: Loads an input image (source) and an image patch (template)
2: Perform a matching procedure template using the function matching procedure
4: Locate the location with a likelihood of adaptation
5: Draw a rectangle around the area corresponding to the highest match
4. Proposed Approch
4.1. The NSSD_DT Algorithm
The proposed Matching model NSSD_DT is based on the updating of the template
according to a sentinel of recognition. The update is triggered with each change of the source in
its geometric form, its scale, Rotation, or occlusion. The tracking begins with an original
template. At the first change of the source that exceeds the sentinel index, the updating is done
by substitution of the old template by the new one.
Figure 5. Sentinel principle
The Normalized sum squared difference algorithm with dynamic template
1: Load an input image (source)
2: Load an image Template
3: Matching
4: If the matching factor <threshold
{
Updated Sizes Template
Restore a new Template
}
5 : Matching
6 : Locate the location with a higher likelihood of adaptation
7: Locate by a rectangle the area of highest correspondence
Matching
Perform a Matching Procedure Model
Template Reconstruction Procedure
Home
{Calls parameter Template (j-1)
Extract Template (j)
Swap Template (j-1) / Template (j)
} End
4.2. Metric of the NSSD_DT algorithm
For efficient tracking without interruption, we have defined a "Sentinel coefficient" of
value monitoring (0.5000). In this section, we discuss the examination of our NSSD_DT
algorithm in two parts: The first part, with real-time video source with constant conditions, a
single target car "racing" and a plain background, we are served by the raised results as a
reference compared to the second metric section. In the second section of the metric, we
treated the examination of NSSD_DT with variables resolutions video, variables number of
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frames per second, occultation, rotation and change of scale. Certain videos combine two or
three variations.
4.3. Metric test with real-time video camera
Test conditions; a single target with a constant background.
This section includes (1) the vertical multiscale tracking test, (2) inclined multi-scale tracking
test, tracking with source rotation test, (3) Tacking with geometrical distortion test and (4)
Tracking with occlusion test.
4.3.1. Vertical Tracking Multi-scale
With a step of 20° of the size, the algorithm submits to the sentinel match value
(0.5000). In order to reach and approach the optimal match value (0.9000), a transposition of
the Template is performed.
Table 1. Vertical Tracking Multi-scale
Figure 5. Vertical Tracking Multi-scale
4.3.2. Inclined multi-scale Tracking
The robustness measurement of the algorithm continues up to 18 % of the moving
object.
Table 2. Inclined Multi-scale Tracking
18% 25% 37% 62% 100%
Figure 6. Inclined multi-scale Tracking
4.3.3. Tracking with source rotation
Table 3. Tracking with Source Rotation
Angle 0° 29° 30° 58° 95° 96° 125° 127°
Coefficient of correspondence 0.89956 0.5001 0.89845 0.5002 0.5000 0.89954 0.5100 0.8948
Recognition Time (s) 0.266 0.268 0.258 0.257 02.62 0.261 0.267 0.266
Angle 155 157° 186° 215° 244° 273° 302° 360°
Coefficient of correspondence 0.5002 0.8956 0.5010 0.8947 0.5005 0.8890 0.5004 0.8991
Recognition Time (s) 0.272 0.268 0.259 0.269 0.265 0.256 0.265 0.262
scale 100% 80%° 60% 40% 20%
correspondence 0.8997 0.6524 0.5236 0.3548 0.2765
Recognition Time (s) 0.261 0.254 0.262 0.225 0.245
scale 100% 62%° 37% 25% 18%
correspondence 0.8548 0.6458 0.5125 0.478 0.2652
Recognition Time (s) 0.241 0.262 0.255 0.245 0.250
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Figure 7. Tracking with source rotation
4.3.4. Tacking with geometrical distortion
Table 4. Tracking with Geometrical Distortion
Figure 8. Tracking with geometrical distortion
4.3.5. Tracking with occlusion
Table 5.Tracking with Occlusion
occlusion Original 20% 28% 48% 60% 64% 75%
correspondence 0.90125 0.7226 0.6482 0.4702 0.3625 0.3245 0.2253
Figure 9. Tracking with occlusion
4.3.6. Comparative Curve of Correspondence tow Algorithme NSSD and NSSD_DT
The values in Table 4 represent the responses of two NSSD and NSSD_DT algorithms
that are tested under test leap by applying the same change to the same sequence at 20fps and
resolution 720 x 1080 with occlusions.
Angle 0° 15° 30° 45°
correspondence 0.89979 0.6524 0.5236 0.3548
Recognition Time (s) 0.261 0.254 0.262 0.225
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Figure 10. Video test for two algorithms NSSD and NSSD
Table 6. Correspondence Values for Two NSSD and NSSD_TD Algorithms
frame 10 45 80 140 255 280 310 335 370 405 437 479
Algorithm
NSSD 0.8845 0.7023 0.6123 0.5861 0.5425 0.5000 0.2132 0.2332 0.2132 0.2148 0.2198 0.2181
Algorithme
NSSD_DT 0.8950 0.8254 0.6578 0.6112 0.5525 0.5000 0.8945 0.7825 0.8561 0.6012 0.5525 0.5000
Images 510 534 570 595 615 640 685 710 742 800 805 900
Algorithm
NSSD
0.2112 0.2325 0.2135 0.2157 0.2132 0.2254 0.2198 0.2101 0.2183 2132 0.2192 0.2153
Algorithm
NSSD_DT 0.8945 0.7892 0.6560 0.6010 0.5635 0.5000 0.8951 0.7236 0.8301 0.6120 0.5501 0.5000
Figure 11. Divergence between the two algorithms NSSD and NSSD_DT
4.4. Evaluation of the results on sequence video
In this section, in order to evaluate our approach, we put our NSSD_DT algorithm
implemented in C++ on 8 different videos at 20 Fps: different resolution (v1), rotation of the
source and the change of scale (v2, v3, v4) and we resume tracking of race car after leaving the
field of vision (v5).
4.4.1 Tracking with fixed template and different resolutions of the source
With a fixed resolution template 1080x1920, we track the targets at different resolution.
Table 7 summarizes the results that represents the average of 10 values raised for each
resolution. The recognition results are evaluated at an average of 84%.
Table 7. Fixed Template and Different Resolutions of the Source
resolution Average recognition rate Average execution time (s)
1080 x 1920 HD 0.8754 0.321
720 x 1080 HD 0.8354 0.295
480 x 720 0.8452 0.261
320 x 480 0.8356 0.257
240 x 320 0.8147 0.243
240 x 144 0.7984 0.222
Average 92.03%
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1080x 720 x 1080
480 x 720 360 x 480
240 x 360 144 x 240
Figure 12. Different resolution video (v1)
4.4.2 Test avec rotation de la source
The test is applied to three industrial videos; v2 to track the madeleine, v3 to track the
car hull on chain and v4 to track the red stylot maintained by the robot bra. Table 8 summarizes
the measurements and gives an average of recognition of 87%.
Table 8. Pen Tracking Table with Rotation and Scale Change
video Fps
Average
recognition rate
Average execution time (s)
V2 20 0.7584 0.262
V3 20 0.8455 0.236
V4 20 0.7562 0.256
Average 87%
Figure 13. Target Rotation
V2
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Figure 14. Target Rotation
Figure 15. Pen tracking with rotation and scaling
4.4.3. Partial Occlusion and Field of View Output Test
The evaluation of our algorithm is replicated 5 times on this sequence. The following
table summarizes the average of recognition and the average of the execution time.
Table 9. The Average Result for Oclusions
Fram
Average
recognition rate
Oclusions
Average
execution time (s)
50 0.8523 0.2562
75 0.7912 0.2623
93 0.7455 0.2365
120 0.7562 0.2563
144 0.6214 Partiel oclusion 0.2652
176 0.2356 Total oclusion 0.2546
198 0.5784 Partiel oclusion 0.2563
215 0.7587 0.2485
Average 74%
V3
V4
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Partial Occlusion Total Occlusion
Figure 16. tracking with scale variation and recovery after quit field
4.5. Recap of Results
Based on the various robustness tests of our tracker, we obtain very relevant results
with an average overall recognition rate of 84%.
Table 10. Recap of Results
Recognition tests Recognition rate
a. Tracking with different resolutions 92%
b. Tracking with source rotation 87%
c. Tracking after quitting the field of view 74%
General Recognition 84 %
5. Discussion
The principle of tracking is based on a vigilant control on the index of similarity. If the
latter reaches the nominal value predefinit (i.e. the sentinel value) then an immediate update of
the template is triggered to continue the tracking with similarity closer to the ideal.
This assumes that the value of the sentinel index must be very well chosen, because a
value close to 1 affects the strength of the tracking. Besides, a value too low than 0.5 risks the
accuracy of target marking. A series of tests for a given subject makes it possible to choose this
famous index. Generally a value near to 0.5 gives satisfactory results.
6. Conclusion
In this work, we have dealt with the problem of tracking visual objects. The goal is to
locate a target over time. In particular we have focused on difficult scenarios in which the object
leaves the field of vision or undergoes important deformations and occlusions. In order to
achieve this objective, we proposed a robust method based on a sentinel response index
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identifying the state of the object and we update the template when it reaches this index. Our
tracker has the performance to detect tracking failure and recover after failure. Therefore, we
solve all problems of deformation and occlusion to ensure a robust tracking in the long term.
Acknowledgements
The authors would like to thank the University of Monastir and the electronics and
microelectronics research laboratory (LR99ES30) for their support in publishing this work.
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