This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is
a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients
contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study
local structure in images. The permutation of Curvelet coefficients from original image and edges image
obtained from gradient operator is used to improve original edges. Experimental results show that this
method brings out details on edges when the decomposition scale increases.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
Geometric wavelet transform for optical flow estimation algorithmijcga
This paper described an algorithm for computing the optical flow (OF) vector of a moving objet in a video sequence based on geometric wavelet transform (GWT). This method tries to calculate the motion between two successive frames by using a GWT. It consists to project the OF vectors on a basis of geometric wavelet. Using GWT for OF estimation has been attracting much attention. This approach takes advantage of the geometric wavelet filter property and requires only two frames. This algorithm is fast and able to estimate the OF with a low-complexity. The technique is suitable for video compression, and can be used for stereo vision and image registration.
Denoising Process Based on Arbitrarily Shaped WindowsCSCJournals
Many factors, such as moving objects, introduce noise in digital images. The presence of noise affects image quality. The image denoising process works on reconstructing a noiseless image and improving its quality. When an image has an additive white Gaussian noise (AWGN) then denoising becomes a challenging process. In our research, we present an improved algorithm for image denoising in the wavelet domain. Homogenous regions for an input image are estimated using a region merging algorithm. The local variance and wavelet shrinkage algorithm are applied to denoise each image patch. Experimental results based on peak signal to noise ratio (PSNR) measurements showed that our algorithm provided better results compared with a denoising algorithm based on a minimum mean square error (MMSE) estimator.
FORGERY (COPY-MOVE) DETECTION IN DIGITAL IMAGES USING BLOCK METHODeditorijcres
AKHILESH KUMAR YADAV, DEENBANDHU SINGH, VIVEK KUMAR
Department of Computer Science and Engineering
Babu Banarasi Das University, Lucknow
akhi2232232@gmail.com, deenbandhusingh85@gmail.com, vivek.kumar0091@gmail.com
ABSTRACT- Digital images can be easily modified using powerful image editing software. Determining whether a manipulation is innocent of sharpening from those which are malicious, such as removing or adding parts to an image is the topic of this paper. In this paper we focus on detection of a special type of forgery-the Copy-Move forgery, in this part of the original image is copied moved to desired location in the same image and pasted. The proposed method compress images using DWT (discrete wavelet transform) and divided into blocks and choose blocks than perform feature vector calculation and lexicographical sorting and duplicated blocks are identified after sorting. This method is good at some manipulation/attack likes scaling, rotation, Gaussian noise, smoothing, JPEG compression etc.
INDEX TERMS- Copy-Move forgery, Wavelet Transform, Lexicographical Sorting, Region Duplication Detection.
Image Registration using NSCT and Invariant MomentCSCJournals
Image registration is a process of matching images, which are taken at different times, from different sensors or from different view points. It is an important step for a great variety of applications such as computer vision, stereo navigation, medical image analysis, pattern recognition and watermarking applications. In this paper an improved feature point selection and matching technique for image registration is proposed. This technique is based on the ability of nonsubsampled contourlet transform (NSCT) to extract significant features irrespective of feature orientation. Then the correspondence between the extracted feature points of reference image and sensed image is achieved using Zernike moments. Feature point pairs are used for estimating the transformation parameters mapping the sensed image to the reference image. Experimental results illustrate the registration accuracy over a wide range for panning and zooming movement and also the robustness of the proposed algorithm to noise. Apart from image registration proposed method can be used for shape matching and object classification. Keywords: Image Registration, NSCT, Contourlet Transform, Zernike Moment.
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScsitconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the
global environment, and in analysing the target detection and recognition .But , segmentation
of (SAR) images is known as a very complex task, due to the existence of speckle noise.
Therefore, in this paper we present a fast SAR images segmentation based on between class
variance. Our choice for used (BCV) method, because it is one of the most effective thresholding
techniques for most real world images with regard to uniformity and shape measures. Our
experiments will be as a test to determine which technique is effective in thresholding
(extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScscpconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the global environment, and in analysing the target detection and recognition .But , segmentation of (SAR) images is known as a very complex task, due to the existence of speckle noise. Therefore, in this paper we present a fast SAR images segmentation based on between class variance. Our choice for used (BCV) method, because it is one of the most effective thresholding techniques for most real world images with regard to uniformity and shape measures. Our experiments will be as a test to determine which technique is effective in thresholding (extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
Geometric wavelet transform for optical flow estimation algorithmijcga
This paper described an algorithm for computing the optical flow (OF) vector of a moving objet in a video sequence based on geometric wavelet transform (GWT). This method tries to calculate the motion between two successive frames by using a GWT. It consists to project the OF vectors on a basis of geometric wavelet. Using GWT for OF estimation has been attracting much attention. This approach takes advantage of the geometric wavelet filter property and requires only two frames. This algorithm is fast and able to estimate the OF with a low-complexity. The technique is suitable for video compression, and can be used for stereo vision and image registration.
Denoising Process Based on Arbitrarily Shaped WindowsCSCJournals
Many factors, such as moving objects, introduce noise in digital images. The presence of noise affects image quality. The image denoising process works on reconstructing a noiseless image and improving its quality. When an image has an additive white Gaussian noise (AWGN) then denoising becomes a challenging process. In our research, we present an improved algorithm for image denoising in the wavelet domain. Homogenous regions for an input image are estimated using a region merging algorithm. The local variance and wavelet shrinkage algorithm are applied to denoise each image patch. Experimental results based on peak signal to noise ratio (PSNR) measurements showed that our algorithm provided better results compared with a denoising algorithm based on a minimum mean square error (MMSE) estimator.
FORGERY (COPY-MOVE) DETECTION IN DIGITAL IMAGES USING BLOCK METHODeditorijcres
AKHILESH KUMAR YADAV, DEENBANDHU SINGH, VIVEK KUMAR
Department of Computer Science and Engineering
Babu Banarasi Das University, Lucknow
akhi2232232@gmail.com, deenbandhusingh85@gmail.com, vivek.kumar0091@gmail.com
ABSTRACT- Digital images can be easily modified using powerful image editing software. Determining whether a manipulation is innocent of sharpening from those which are malicious, such as removing or adding parts to an image is the topic of this paper. In this paper we focus on detection of a special type of forgery-the Copy-Move forgery, in this part of the original image is copied moved to desired location in the same image and pasted. The proposed method compress images using DWT (discrete wavelet transform) and divided into blocks and choose blocks than perform feature vector calculation and lexicographical sorting and duplicated blocks are identified after sorting. This method is good at some manipulation/attack likes scaling, rotation, Gaussian noise, smoothing, JPEG compression etc.
INDEX TERMS- Copy-Move forgery, Wavelet Transform, Lexicographical Sorting, Region Duplication Detection.
Image Registration using NSCT and Invariant MomentCSCJournals
Image registration is a process of matching images, which are taken at different times, from different sensors or from different view points. It is an important step for a great variety of applications such as computer vision, stereo navigation, medical image analysis, pattern recognition and watermarking applications. In this paper an improved feature point selection and matching technique for image registration is proposed. This technique is based on the ability of nonsubsampled contourlet transform (NSCT) to extract significant features irrespective of feature orientation. Then the correspondence between the extracted feature points of reference image and sensed image is achieved using Zernike moments. Feature point pairs are used for estimating the transformation parameters mapping the sensed image to the reference image. Experimental results illustrate the registration accuracy over a wide range for panning and zooming movement and also the robustness of the proposed algorithm to noise. Apart from image registration proposed method can be used for shape matching and object classification. Keywords: Image Registration, NSCT, Contourlet Transform, Zernike Moment.
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScsitconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the
global environment, and in analysing the target detection and recognition .But , segmentation
of (SAR) images is known as a very complex task, due to the existence of speckle noise.
Therefore, in this paper we present a fast SAR images segmentation based on between class
variance. Our choice for used (BCV) method, because it is one of the most effective thresholding
techniques for most real world images with regard to uniformity and shape measures. Our
experiments will be as a test to determine which technique is effective in thresholding
(extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScscpconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the global environment, and in analysing the target detection and recognition .But , segmentation of (SAR) images is known as a very complex task, due to the existence of speckle noise. Therefore, in this paper we present a fast SAR images segmentation based on between class variance. Our choice for used (BCV) method, because it is one of the most effective thresholding techniques for most real world images with regard to uniformity and shape measures. Our experiments will be as a test to determine which technique is effective in thresholding (extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
Remote sensing image fusion using contourlet transform with sharp frequency l...Zac Darcy
This paper addresses four different aspects of the remote sensing image fusion: i) image fusion method, ii)
quality analysis of fusion results, iii) effects of image decomposition level, and iv) importance of image
registration. First, a new contourlet-based image fusion method is presented, which is an improvement
over the wavelet-based fusion. This fusion method is then utilized withinthe main fusion process to analyze
the final fusion results. Fusion framework, scheme and datasets used in the study are discussed in detail.
Second, quality analysis of the fusion results is discussed using various quantitative metrics for both spatial
and spectral analyses. Our results indicate that the proposed contourlet-based fusion method performs
better than the conventional wavelet-based fusion methodsin terms of both spatial and spectral analyses.
Third, we conducted an analysis on the effects of the image decomposition level and observed that the
decomposition level of 3 produced better fusion results than both smaller and greater number of levels.
Last, we created four different fusion scenarios to examine the importance of the image registration. As a
result, the feature-based image registration using the edge features of the source images produced better
fusion results than the intensity-based imageregistration.
Performance of Efficient Closed-Form Solution to Comprehensive Frontier Exposureiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Copy-Rotate-Move Forgery Detection Based on Spatial DomainSondosFadl
we propose a method which is efficient and fast for detecting Copy-Move regions even when the copied region was undergone rotation modify in spatial domain.
Segmentation by Fusion of Self-Adaptive SFCM Cluster in Multi-Color Space Com...CSCJournals
This paper proposes a new, simple, and efficient segmentation approach that could find diverse applications in pattern recognition as well as in computer vision, particularly in color image segmentation. First, we choose the best segmentation components among six different color spaces. Then, Histogram and SFCM techniques are applied for initialization of segmentation. Finally, we fuse the segmentation results and merge similar regions. Extensive experiments have been taken on Berkeley image database by using the proposed algorithm. The results show that, compared with some classical segmentation algorithms, such as Mean-Shift, FCR and CTM, etc, our method could yield reasonably good or better image partitioning, which illustrates practical value of the method.
Improved nonlocal means based on pre classification and invariant block matchingIAEME Publication
One of the most popular image denoising methods based on self-similarity is called nonlocal
means (NLM). Though it can achieve remarkable performance, this method has a few shortcomings,
e.g., the computationally expensive calculation of the similarity measure, and the lack of reliable
candidates for some non repetitive patches. In this paper, we propose to improve NLM by integrating
Gaussian blur, clustering, and row image weighted averaging into the NLM framework.
Experimental results show that the proposed technique can perform denoising better than the original
NLM both quantitatively and visually, especially when the noise level is high.
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUEScscpconf
In the first study [1], a combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map were used to perform image segmentation and edge detection
tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging
procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain accurate edge maps of our images without using watershed method. In this technique: We solved the problem of undesirable over segmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image. In the 2nd study level set methods are used for the implementation of curve/interface evolution under various forces. In the third study the main idea is to detect regions (objects) boundaries, to isolate and extract individual components from a medical image. This is done using an active contours to detect regions in a given image, based on techniques of curve evolution, Mumford–Shah functional for segmentation and level sets. Once we classified our images into different intensity regions based on Markov Random Field. Then we detect regions whose boundaries are not necessarily defined by gradient by minimize an energy of Mumford–Shah functional forsegmentation, where in the level set formulation, the problem becomes a mean-curvature which will stop on the desired boundary. The stopping term does not depend on the gradient of the image as in the classical active contour. The initial curve of level set can be anywhere in the image, and interior contours are automatically detected. The final image segmentation is one
closed boundary per actual region in the image.
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.
Region duplication forgery detection in digital imagesRupesh Ambatwad
Region duplication or copy move forgery is a common type of tampering scheme carried out to create a fake image. The field on blind image forensics depends upon the authenticity of the digital image. As in copy move forgery the duplicated region belongs to the same image, the detection of tampering is complex as it does not leave a visual clue. But the tampering gives rise to glitches at pixel level
Abstract: Many applications such as robot navigation, defense, medical and remote sensing performvarious processing tasks, which can be performed more easily when all objects in different images of the same scene are combined into a single fused image. In this paper, we propose a fast and effective method for image fusion. The proposed method derives the intensity based variations that is large and small scale, from the source images. In this approach, guided filtering is employed for this extraction. Gaussian and Laplacian pyramidal approach is then used to fuse the different layers obtained. Experimental results demonstrate that the proposed method can obtain better performance for fusion of
all sets of images. The results clearly indicate the feasibility of the proposed approach.
A Hybrid Technique for the Automated Segmentation of Corpus Callosum in Midsa...IJERA Editor
The corpus callosum (CC) is the largest white-matter structure in human brain. In this paper, we take two techniques to observe the results of segmentation of Corpus Callosum. The first one is mean shift algorithm and morphological operation. The second one is k-means clustering. In this paper, it is performed in three steps. The first step is finding the corpus callosum area using adaptive mean shift algorithm or k-means clustering . In second step, the boundary of detected CC area is then used as the initial contour in the Geometric Active Contour (GAC) mode and final step to remove unknown noise using morphological operation and evolved to get the final segmentation result. The experimental results demonstrate that the mean shift algorithm and k-means clustering has provided a reliable segmentation performance.
Change Detection of Water-Body in Synthetic Aperture Radar ImagesCSCJournals
Change detection is the art of quantifying the changes in the Synthetic Aperture Radar (SAR) images that have happened over a period of time. Remote sensing has been the parental technique to perform change detection analysis. This paper empirically investigates the impact of applying the combination of texture features for different classification techniques to separate water body from non-water body. At first, the images are classified using unsupervised Principle Component Analysis (PCA) based K-means clustering for dimension reduction. Then the texture features like Energy, Entropy, Contrast , Inverse Differential Moment , Directional Moment and the Median are extracted using Gray Level Co-occurrence Matrix (GLCM) and these features are utilized in Linear Vector Quantization (LVQ) and Support Vector Machine (SVM) classifiers. This paper aims to apply a combination of the texture features in order to significantly improve the accuracy of detection. The utility of detection analysis, influences management and policy decision making for long-term construction projects by predicting the preventable losses.
Performance Evaluation of Quarter Shift Dual Tree Complex Wavelet Transform B...IJECEIAES
In this paper, multifocus image fusion using quarter shift dual tree complex wavelet transform is proposed. Multifocus image fusion is a technique that combines the partially focused regions of multiple images of the same scene into a fully focused fused image. Directional selectivity and shift invariance properties are essential to produce a high quality fused image. However conventional wavelet based fusion algorithms introduce the ringing artifacts into fused image due to lack of shift invariance and poor directionality. The quarter shift dual tree complex wavelet transform has proven to be an effective multi-resolution transform for image fusion with its directional and shift invariant properties. Experimentation with this transform led to the conclusion that the proposed method not only produce sharp details (focused regions) in fused image due to its good directionality but also removes artifacts with its shift invariance in order to get high quality fused image. Proposed method performance is compared with traditional fusion methods in terms of objective measures.
A modified distance regularized level set model for liver segmentation from c...sipij
Segmentation of organs from medical images is an active and interesting area of research. Liver segmentation incurs more challenges and difficulties compared with segmentation of other organs. In this paper we demonstrate a liver segmentation method for computer tomography images. We revisit the distance regularization level set (DRLS) model by deploying new balloon forces. These forces control the direction of the evolution and slow down the evolution process in regions that are associated with weak or without edges. The newly added balloon forces discourage the evolving contour from exceeding the liver
boundary or leaking at a region that is associated with a weak edge, or does not have an edge. Our
experimental results confirm that the method yields a satisfactory overall segmentation outcome. Comparing with the original DRLS model, our model is proven to be more effective in handling oversegmentation problems.
A divisive hierarchical clustering based method for indexing image informationsipij
In most practical applications of image retrieval, high-dimensional feature vectors are required, but current multi-dimensional indexing structures lose their efficiency with growth of dimensions. Our goal is to propose a divisive hierarchical clustering-based multi-dimensional indexing structure which is efficient in high-dimensional feature spaces. A projection pursuit method has been used for finding a component of the data, which data's projections onto it maximizes the approximation of negentropy for preparing essential information in order to partitioning of the data space. Various tests and experimental results on high-dimensional datasets indicate the performance of proposed method in comparison with others.
My Best friend Izaiah Kaine Breeden....recently left this world..and he got what he wanted..he is flying with the angels.....Rest In Paradise Sweet bubbles...Buttercup loves you</3
Digital camera and mobile document image acquisition are new trends arising in the world of Optical Character Recognition and text detection. In some cases, such process integrates many distortions and produces poorly scanned text or text-photo images and natural images, leading to an unreliable OCR digitization. In this paper, we present a novel nonparametric and unsupervised method to compensate for
undesirable document image distortions aiming to optimally improve OCR accuracy. Our approach relies on a very efficient stack of document image enhancing techniques to recover deformation of the entire document image. First, we propose a local brightness and contrast adjustment method to effectively handle lighting variations and the irregular distribution of image illumination. Second, we use an optimized greyscale conversion algorithm to transform our document image to greyscale level. Third, we sharpen the
useful information in the resulting greyscale image using Un-sharp Masking method. Finally, an optimal global binarization approach is used to prepare the final document image to OCR recognition. The proposed approach can significantly improve text detection rate and optical character recognition
accuracy. To demonstrate the efficiency of our approach, an exhaustive experimentation on a standard dataset is presented.
Remote sensing image fusion using contourlet transform with sharp frequency l...Zac Darcy
This paper addresses four different aspects of the remote sensing image fusion: i) image fusion method, ii)
quality analysis of fusion results, iii) effects of image decomposition level, and iv) importance of image
registration. First, a new contourlet-based image fusion method is presented, which is an improvement
over the wavelet-based fusion. This fusion method is then utilized withinthe main fusion process to analyze
the final fusion results. Fusion framework, scheme and datasets used in the study are discussed in detail.
Second, quality analysis of the fusion results is discussed using various quantitative metrics for both spatial
and spectral analyses. Our results indicate that the proposed contourlet-based fusion method performs
better than the conventional wavelet-based fusion methodsin terms of both spatial and spectral analyses.
Third, we conducted an analysis on the effects of the image decomposition level and observed that the
decomposition level of 3 produced better fusion results than both smaller and greater number of levels.
Last, we created four different fusion scenarios to examine the importance of the image registration. As a
result, the feature-based image registration using the edge features of the source images produced better
fusion results than the intensity-based imageregistration.
Performance of Efficient Closed-Form Solution to Comprehensive Frontier Exposureiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Copy-Rotate-Move Forgery Detection Based on Spatial DomainSondosFadl
we propose a method which is efficient and fast for detecting Copy-Move regions even when the copied region was undergone rotation modify in spatial domain.
Segmentation by Fusion of Self-Adaptive SFCM Cluster in Multi-Color Space Com...CSCJournals
This paper proposes a new, simple, and efficient segmentation approach that could find diverse applications in pattern recognition as well as in computer vision, particularly in color image segmentation. First, we choose the best segmentation components among six different color spaces. Then, Histogram and SFCM techniques are applied for initialization of segmentation. Finally, we fuse the segmentation results and merge similar regions. Extensive experiments have been taken on Berkeley image database by using the proposed algorithm. The results show that, compared with some classical segmentation algorithms, such as Mean-Shift, FCR and CTM, etc, our method could yield reasonably good or better image partitioning, which illustrates practical value of the method.
Improved nonlocal means based on pre classification and invariant block matchingIAEME Publication
One of the most popular image denoising methods based on self-similarity is called nonlocal
means (NLM). Though it can achieve remarkable performance, this method has a few shortcomings,
e.g., the computationally expensive calculation of the similarity measure, and the lack of reliable
candidates for some non repetitive patches. In this paper, we propose to improve NLM by integrating
Gaussian blur, clustering, and row image weighted averaging into the NLM framework.
Experimental results show that the proposed technique can perform denoising better than the original
NLM both quantitatively and visually, especially when the noise level is high.
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUEScscpconf
In the first study [1], a combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map were used to perform image segmentation and edge detection
tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging
procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain accurate edge maps of our images without using watershed method. In this technique: We solved the problem of undesirable over segmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image. In the 2nd study level set methods are used for the implementation of curve/interface evolution under various forces. In the third study the main idea is to detect regions (objects) boundaries, to isolate and extract individual components from a medical image. This is done using an active contours to detect regions in a given image, based on techniques of curve evolution, Mumford–Shah functional for segmentation and level sets. Once we classified our images into different intensity regions based on Markov Random Field. Then we detect regions whose boundaries are not necessarily defined by gradient by minimize an energy of Mumford–Shah functional forsegmentation, where in the level set formulation, the problem becomes a mean-curvature which will stop on the desired boundary. The stopping term does not depend on the gradient of the image as in the classical active contour. The initial curve of level set can be anywhere in the image, and interior contours are automatically detected. The final image segmentation is one
closed boundary per actual region in the image.
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.
Region duplication forgery detection in digital imagesRupesh Ambatwad
Region duplication or copy move forgery is a common type of tampering scheme carried out to create a fake image. The field on blind image forensics depends upon the authenticity of the digital image. As in copy move forgery the duplicated region belongs to the same image, the detection of tampering is complex as it does not leave a visual clue. But the tampering gives rise to glitches at pixel level
Abstract: Many applications such as robot navigation, defense, medical and remote sensing performvarious processing tasks, which can be performed more easily when all objects in different images of the same scene are combined into a single fused image. In this paper, we propose a fast and effective method for image fusion. The proposed method derives the intensity based variations that is large and small scale, from the source images. In this approach, guided filtering is employed for this extraction. Gaussian and Laplacian pyramidal approach is then used to fuse the different layers obtained. Experimental results demonstrate that the proposed method can obtain better performance for fusion of
all sets of images. The results clearly indicate the feasibility of the proposed approach.
A Hybrid Technique for the Automated Segmentation of Corpus Callosum in Midsa...IJERA Editor
The corpus callosum (CC) is the largest white-matter structure in human brain. In this paper, we take two techniques to observe the results of segmentation of Corpus Callosum. The first one is mean shift algorithm and morphological operation. The second one is k-means clustering. In this paper, it is performed in three steps. The first step is finding the corpus callosum area using adaptive mean shift algorithm or k-means clustering . In second step, the boundary of detected CC area is then used as the initial contour in the Geometric Active Contour (GAC) mode and final step to remove unknown noise using morphological operation and evolved to get the final segmentation result. The experimental results demonstrate that the mean shift algorithm and k-means clustering has provided a reliable segmentation performance.
Change Detection of Water-Body in Synthetic Aperture Radar ImagesCSCJournals
Change detection is the art of quantifying the changes in the Synthetic Aperture Radar (SAR) images that have happened over a period of time. Remote sensing has been the parental technique to perform change detection analysis. This paper empirically investigates the impact of applying the combination of texture features for different classification techniques to separate water body from non-water body. At first, the images are classified using unsupervised Principle Component Analysis (PCA) based K-means clustering for dimension reduction. Then the texture features like Energy, Entropy, Contrast , Inverse Differential Moment , Directional Moment and the Median are extracted using Gray Level Co-occurrence Matrix (GLCM) and these features are utilized in Linear Vector Quantization (LVQ) and Support Vector Machine (SVM) classifiers. This paper aims to apply a combination of the texture features in order to significantly improve the accuracy of detection. The utility of detection analysis, influences management and policy decision making for long-term construction projects by predicting the preventable losses.
Performance Evaluation of Quarter Shift Dual Tree Complex Wavelet Transform B...IJECEIAES
In this paper, multifocus image fusion using quarter shift dual tree complex wavelet transform is proposed. Multifocus image fusion is a technique that combines the partially focused regions of multiple images of the same scene into a fully focused fused image. Directional selectivity and shift invariance properties are essential to produce a high quality fused image. However conventional wavelet based fusion algorithms introduce the ringing artifacts into fused image due to lack of shift invariance and poor directionality. The quarter shift dual tree complex wavelet transform has proven to be an effective multi-resolution transform for image fusion with its directional and shift invariant properties. Experimentation with this transform led to the conclusion that the proposed method not only produce sharp details (focused regions) in fused image due to its good directionality but also removes artifacts with its shift invariance in order to get high quality fused image. Proposed method performance is compared with traditional fusion methods in terms of objective measures.
A modified distance regularized level set model for liver segmentation from c...sipij
Segmentation of organs from medical images is an active and interesting area of research. Liver segmentation incurs more challenges and difficulties compared with segmentation of other organs. In this paper we demonstrate a liver segmentation method for computer tomography images. We revisit the distance regularization level set (DRLS) model by deploying new balloon forces. These forces control the direction of the evolution and slow down the evolution process in regions that are associated with weak or without edges. The newly added balloon forces discourage the evolving contour from exceeding the liver
boundary or leaking at a region that is associated with a weak edge, or does not have an edge. Our
experimental results confirm that the method yields a satisfactory overall segmentation outcome. Comparing with the original DRLS model, our model is proven to be more effective in handling oversegmentation problems.
A divisive hierarchical clustering based method for indexing image informationsipij
In most practical applications of image retrieval, high-dimensional feature vectors are required, but current multi-dimensional indexing structures lose their efficiency with growth of dimensions. Our goal is to propose a divisive hierarchical clustering-based multi-dimensional indexing structure which is efficient in high-dimensional feature spaces. A projection pursuit method has been used for finding a component of the data, which data's projections onto it maximizes the approximation of negentropy for preparing essential information in order to partitioning of the data space. Various tests and experimental results on high-dimensional datasets indicate the performance of proposed method in comparison with others.
My Best friend Izaiah Kaine Breeden....recently left this world..and he got what he wanted..he is flying with the angels.....Rest In Paradise Sweet bubbles...Buttercup loves you</3
Digital camera and mobile document image acquisition are new trends arising in the world of Optical Character Recognition and text detection. In some cases, such process integrates many distortions and produces poorly scanned text or text-photo images and natural images, leading to an unreliable OCR digitization. In this paper, we present a novel nonparametric and unsupervised method to compensate for
undesirable document image distortions aiming to optimally improve OCR accuracy. Our approach relies on a very efficient stack of document image enhancing techniques to recover deformation of the entire document image. First, we propose a local brightness and contrast adjustment method to effectively handle lighting variations and the irregular distribution of image illumination. Second, we use an optimized greyscale conversion algorithm to transform our document image to greyscale level. Third, we sharpen the
useful information in the resulting greyscale image using Un-sharp Masking method. Finally, an optimal global binarization approach is used to prepare the final document image to OCR recognition. The proposed approach can significantly improve text detection rate and optical character recognition
accuracy. To demonstrate the efficiency of our approach, an exhaustive experimentation on a standard dataset is presented.
Time of arrival based localization in wireless sensor networks a non linear ...sipij
In this paper, we aim to obtain the location information of a sensor node deployed in a Wireless Sensor Network (WSN). Here, Time of Arrival based localization technique is considered. We calculate the position information of an unknown sensor node using the non- linear techniques. The performances of the techniques are compared with the Cramer Rao Lower bound (CRLB). Non-linear Least Squares and the Maximum Likelihood are the non-linear techniques that have been used to estimate the position of the unknown sensor node. Each of these non-linear techniques are iterative approaches, namely, Newton
Raphson estimate, Gauss Newton Estimate and the Steepest Descent estimate for comparison. Based on the
results of the simulation, the approaches have been compared. From the simulation study, Localization
based on Maximum Likelihood approach is having higher localization accuracy.
Routing in All-Optical Networks Using Recursive State Space Techniquesipij
In this papr, we have minimized the effects of failures on network performace, by using suitable Routing
and Wavelenghth Assignment(RWA) method without disturbing other performance criteria such as blocking
probability(BP) and network management(NM). The computation complexity is reduced by using Kalaman
Filter(KF) techniques. The minimum reconfiguration probability routing (MRPR) algorithm must be
able to select most reliable routes and assign wavelengths to connections in a manner that utilizes the light
path(LP) established efficiently considering all possible requests.
NIRS-BASED CORTICAL ACTIVATION ANALYSIS BY TEMPORAL CROSS CORRELATIONsipij
In this study we present a method of signal processing to determine dominant channels in near infrared spectroscopy (NIRS). To compare measuring channels and identify delays between them, cross correlation is computed. Furthermore, to find out possible dominant channels, a visual inspection was performed. The
outcomes demonstrated that the visual inspection exhibited evoked-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with comparable studies and the cross correlation study discovered dominant channels on both cerebral hemispheres. The analysis also showed a relationship between dominant channels and adjacent channels. For that reason, our results present a new
method to identify dominant regions in the cerebral cortex using near-infrared spectroscopy. These findings have also implications in the decrease of channels by eliminating irrelevant channels for the experiment.
The state-of-the-art Automatic Speech Recognition (ASR) systems lack the ability to identify spoken words if they have non-standard pronunciations. In this paper, we present a new classification algorithm to identify pronunciation variants. It uses Dynamic Phone Warping (DPW) technique to compute the
pronunciation-by-pronunciation phonetic distance and a threshold critical distance criterion for the classification. The proposed method consists of two steps; a training step to estimate a critical distance
parameter using transcribed data and in the second step, use this critical distance criterion to classify the input utterances into the pronunciation variants and OOV words.
The algorithm is implemented using Java language. The classifier is trained on data sets from TIMIT
speech corpus and CMU pronunciation dictionary. The confusion matrix and precision, recall and accuracy performance metrics are used for the performance evaluation. Experimental results show significant performance improvement over the existing classifiers.
USING CRM SOLUTIONS TO INCREASE SALES AND RETAIN VALUABLE CUSTOMERSInsideUp
CRM is an important tool for creating business success. The better your customer relationships, the
easier it is to conduct business and generate revenue. Using available technology to improve
customer relations management simply makes good business sense.
If you need further information, or would like assistance in selecting a CRM provider, go to
http://www.insideup.com/compare/customer_relationship_management
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.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
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.
A Comparative Study of Wavelet and Curvelet Transform for Image DenoisingIOSR Journals
Abstract : This paper describes a comparison of the discriminating power of the various multiresolution based thresholding techniques i.e., Wavelet, curve let for image denoising.Curvelet transform offer exact reconstruction, stability against perturbation, ease of implementation and low computational complexity. We propose to employ curve let for facial feature extraction and perform a thorough comparison against wavelet transform; especially, the orientation of curve let is analysed. Experiments show that for expression changes, the small scale coefficients of curve let transform are robust, though the large scale coefficients of both transform are likely influenced. The reason behind the advantages of curvelet lies in its abilities of sparse representation that are critical for compression, estimation of images which are denoised and its inverse problems, thus the experiments and theoretical analysis coincide . Keywords: Curvelet transform, Face recognition, Feature extraction, Sparse representation Thresholding rules,Wavelet transform..
2-Dimensional Wavelet pre-processing to extract IC-Pin information for disarr...IOSR Journals
Abstract: Due to higher processing power to cost ratio, it is now possible to replace the manual detection methods used in the IC (Integrated Circuit) industry by Image-processing based automated methods, to detect a broken pin of an IC connected on a PCB during manufacturing, which will make the process faster, easier and cheaper. In this paper an accurate and fast automatic detection method is used where the top view camera shots of PCBs are processed using advanced methods of 2-dimensional discrete wavelet pre-processing before applying edge-detection. Comparison with conventional edge detection methods such as Sobel, Prewitt and Canny edge detection without 2-D DWT is also performed. Keywords :2-dimensional wavelets, Edge detection, Machine vision, Image processing, Canny.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Denoising and Edge Detection Using SobelmethodIJMER
The main aim of our study is to detect edges in the image without any noise , In many of the images edges carry important information of the image, this paper presents a method which consists of sobel operator and discrete wavelet de-noising to do edge detection on images which include white Gaussian noises. There were so many methods for the edge detection, sobel is the one of the method, by using this sobel operator or median filtering, salt and pepper noise cannot be removed properly, so firstly we use complex wavelet to remove noise and sobel operator is used to do edge detection on the image. Through the pictures obtained by the experiment, we can observe that compared to other methods, the method has more obvious effect on edge detection.
SINGLE‐PHASE TO THREE‐PHASE DRIVE SYSTEM USING TWO PARALLEL SINGLE‐PHASE RECT...ijiert bestjournal
Now a days digital image processing is rapid emerging field with fast growing
applications in sciences and engineering technologies. Digital image processing has broad
spectrum of applications such as remote sensing, medical processing, radar, sonar,
robotics, sport field and automated processes [1-2]. Edge detection techniques are
employed for the detecting the edges of the primitive picture. Earlier some primitive
methods were used for the image processing. H. C. Andrew et.al. gave the method of
digital image restoration [3-5], A. K. Jain and et.al put forwarded the partial difference
equations and finite differences in image processing [6]. Image process, image models and
estimation regarding the edge detection has been flourished during last decade [7-9]. Most
modules in practical vision system depend, directly or indirectly, on the performance of an
edge detector and digital image processing.
ALEXANDER FRACTIONAL INTEGRAL FILTERING OF WAVELET COEFFICIENTS FOR IMAGE DEN...sipij
The present paper, proposes an efficient denoising algorithm which works well for images corrupted with
Gaussian and speckle noise. The denoising algorithm utilizes the alexander fractional integral filter which
works by the construction of fractional masks window computed using alexander polynomial. Prior to the
application of the designed filter, the corrupted image is decomposed using symlet wavelet from which only
the horizontal, vertical and diagonal components are denoised using the alexander integral filter.
Significant increase in the reconstruction quality was noticed when the approach was applied on the
wavelet decomposed image rather than applying it directly on the noisy image. Quantitatively the results
are evaluated using the peak signal to noise ratio (PSNR) which was 30.8059 on an average for images
corrupted with Gaussian noise and 36.52 for images corrupted with speckle noise, which clearly
outperforms the existing methods.
Fusion Based Gaussian noise Removal in the Images using Curvelets and Wavelet...CSCJournals
Curvelets denoise approach has been widely used in many fields for its ability to obtain high quality result images.Curvelet transform is superior to wavelet in the expression of image edge, such as geometry characteristic of curve, which has already obtained good results in image denoising. However artifacts those appear in the result images of Curvelets approach prevent its application in some fields such as medical image. This paper puts forward a fusion based method because certain regions of the image have the ringing and radial stripe after Curvelets transform. The experimental results indicate that fusion method has an abroad future for eliminating the noise of images. The results of the algorithm applied to ultrasonic medical images also indicate that the algorithm can be used efficiently in medical image fields.
Determining the Efficient Subband Coefficients of Biorthogonal Wavelet for Gr...CSCJournals
In this paper, we propose an invisible blind watermarking scheme for the gray-level images. The cover image is decomposed using the Discrete Wavelet Transform with Biorthogonal wavelet filters and the watermark is embedded into significant coefficients of the transformation. The Biorthogonal wavelet is used because it has the property of perfect reconstruction and smoothness. The proposed scheme embeds a monochrome watermark into a gray-level image. In the embedding process, we use a localized decomposition, means that the second level decomposition is performed on the detail sub-band resulting from the first level decomposition. The image is decomposed into first level and for second level decomposition we consider Horizontal, vertical and diagonal subband separately. From this second level decomposition we take the respective Horizontal, vertical and diagonal coefficients for embedding the watermark. The robustness of the scheme is tested by considering the different types of image processing attacks like blurring, cropping, sharpening, Gaussian filtering and salt and pepper noise effect. The experimental result shows that the embedding watermark into diagonal subband coefficients is robust against different types of attacks.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Segmentation Based Multilevel Wide Band Compression for SAR Images Using Coif...CSCJournals
Synthetic aperture radar (SAR) data represents a significant resource of information for a large variety of researchers. Thus, there is a strong interest in developing data encoding and decoding algorithms which can obtain higher compression ratios while keeping image quality to an acceptable level. In this work, results of different wavelet-based image compression and segmentation based wavelet image compression are assessed through controlled experiments on synthetic SAR images. The effects of dissimilar wavelet functions, number of decompositions are examined in order to find optimal family for SAR images. The choice of optimal wavelets in segmentation based wavelet image compression is coiflet for low frequency and high frequency component. The results presented here is a good reference for SAR application developers to choose the wavelet families and also it concludes that wavelets transform is rapid, robust and reliable tool for SAR image compression. Numerical results confirm the potency of this approach.
Mislaid character analysis using 2-dimensional discrete wavelet transform for...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Boosting CED Using Robust Orientation Estimationijma
n this paper, Coherence Enhancement Diffusion (CED) is boosted feeding external orientation using new
robust orientation estimation. In CED, proper scale selection is very important as the gradient vector at
that scale reflects the orientation of local ridge. For this purpose a new scheme is proposed in which pre
calculated orientation, by using local and integration scales. From the experiments it is found the proposed
scheme is working much better in noisy environment as compared to the traditional Coherence
Enhancement Diffusion
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Epistemic Interaction - tuning interfaces to provide information for AI support
ANALYSIS OF INTEREST POINTS OF CURVELET COEFFICIENTS CONTRIBUTIONS OF MICROSCOPIC IMAGES AND IMPROVEMENT OF EDGES
1. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.2, April 2013
DOI : 10.5121/sipij.2013.4201 1
ANALYSIS OF INTEREST POINTS OF CURVELET
COEFFICIENTS CONTRIBUTIONS OF MICROSCOPIC
IMAGES AND IMPROVEMENT OF EDGES
A. DJIMELI1
, D. TCHIOTSOP2
and R. TCHINDA3
1
Department of Telecommunication and Network engineering, Laboratoire
d'Automatique et d'Informatique Appliquée (LAIA), IUT-FV, University of Dschang
adjimeli@yahoo.fr
2
Department of Electrical engineering, Laboratoire d'Automatique et d'Informatique
Appliquée (LAIA), IUT-FV, University of Dschang
dtchiot@yahoo.fr
3
Laboratoire d'Ingénierie des Systèmes Industriels et de l'Environnement (LISIE), IUT-
FV, University of Dschang
ttchinda@yahoo.fr
ABSTRACT
This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is
a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients
contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study
local structure in images. The permutation of Curvelet coefficients from original image and edges image
obtained from gradient operator is used to improve original edges. Experimental results show that this
method brings out details on edges when the decomposition scale increases.
KEYWORDS
Curvelet Coefficients, SIFT interest points, Edge Improvement,
1. INTRODUCTION
Edges are discontinuity in the image map resulting from sudden changes of the texture. Contours
also mark high frequencies areas in an image. Contours are very important in the characterization
of physiognomies and perception. Consequently they are call upon in many applications of image
processing such as the analysis of detail in an image, content based retrieval, objects
reconstruction and objects recognition.
In practice, contours are determined using filtering operation by applying a convolution of the
image with a mask of filter. For this purpose, several masks of convolutions have been defined.
Some examples are the filter of Roberts, the filter of Sobel and the filter of Prewitt [1,2].
Unfortunately these filters are sensitive to the noise and they detect contours only in two
directions. The Kirsch method uses eight filters to calculate contours in eight different directions,
including derivatives in these directions. However the filters shunting devices give sometimes
thick contours. By making a convolution of an image with the Laplacian of Gaussian, Marr and
Hildreth [2] remove noise which would have been detected by the Laplacian. The success of their
method lies in the good choice of the variance σ. Canny et al. proposed an optimal filter for the
detection of an ideal contour drowned in a white vibration Gaussien. The filter proposed is
optimal in localization and maximizes the signal report ratio on noise [3].
2. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.2, April 2013
2
Conventional filtering edges detection techniques operate only in one scale. Multiresolution
analysis overcomes many challenges in many domains. Meanwhile separable wavelets are
isotropic and could not get edge regularity in an image [4]. This is the main reason why many
geometrical transforms have appeared [4,5,6,7]. Curvelet transform [7,8] is non adaptative
geometrical transform that has been used with great success in many applications such as
denoising, shape recognition, edge detection and enhancement [7,9,10,11,12,13,14,15,16].
Gebäck and Koumoutsakos used the discrete curvelet transform to extract information about
directions and magnitudes of features in the image at selected levels of details [14]. The edges are
obtained using the non-maximal suppression and hysteresis thresholding of the Canny algorithm.
In order to enhance the edges of an image Liu and Qiu have used different means to deal with
different scales of the coefficients to enhance the edge of image [15]. The best recent edge
detectors through traditional methods start by improving the image quality with Curvelet
transform [15,16]. The analysis of Curvelet coefficients contributions in image processing can
found in [15,16,18]. Unfortunately authors have not been interested enough on local structure of
Curvelet coefficients contributions. To our knowledge, no paper tackles the question of edge
improvement of the traditional methods. Meanwhile the potentials of Curvelet decomposition
have not been exploited enough. In this paper we analyze local properties of Curvelet coefficients
contributions through Scale Invariant Feature Transform (SIFT) [17] and use this study to
improve edges in microscopic images.
This paper is organized as follows. In the second section we briefly present Curvelet transform.
We describe in section three the coefficients contributions of Curvelet transform in analysing
microscopic images. Section four brings out the edge detection algorithm using Curvelet
transform and we conclude our work in section five.
2. CURVELET TRANSFORM
Two windows are useful to describe Curvelet [7]. Let us consider in continuous domain ℝ2,
( )W r the radial window with value [ ]1/ 2,2r ∈ and ( , )V r t the angular window with value
[ ]1,1t ∈ − . Admissibility conditions are defined by (1) and (2), where j is a radial variable
and l is an angular variable.
2
(2 ) 1,j
j
W r
+∞
=−∞
=∑
3 3
,
4 2
r
∈
(1)
2
( ) 1,
l
V t l
+∞
=−∞
− =∑
1 1
,
2 2
t
∈ −
(2)
Figure 1. Curvelet tiling of space and frequency where lengths and widths obeying the scaling
law 2
width length≈
3. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.2, April 2013
3
A frequency window jU is defined by (3) for 0j j≥ in Fourier domain. The Curvelet
transform represents a curve as a superposition of functions of various lengths and widths
obeying the scaling law 2
width length≈ . An example of windowing is represented in figure 1
where circular wedge depends on the scale and the direction.
/2
3 /4 2
( , ) 2 (2 ) ( )
2
j
j j
jU r W r V
θ
θ
π
−
= (3)
with / 2j being the integer part of / 2j .
Curvelet real values can then be computed as:
( , ) ( , )jU r U rθ θ π+ + (4)
frequency domain Curvelets are supported near a parabolic wedge. However rotation is not
adapted to Cartesian array. That is why Candès and Al indexed the values to the origin. Figure 2.
(a) shows grey data in the upright parallelogram broken and indexed in a rectangle at the origin.
For Curvelet digitalization, they redefine a new wedge jW% based on concentric squares as shown
in figure 2. (b) and expressed by (5) [8].
2 2
1( ) ( ) ( )j J jW ω ω ω+= Φ − Φ% , 0j ≥ (5)
Where Φ is defined by the product of two monodimensional bypass windows.
(a) (b)
Figure 2. (a) Indexation of value to the origin in Cartesian domain, (b) digital tailing of Curvelet.
Φ is useful to separate scales in Cartesian domain. Thus in discrete curvelet Transform, radial
and angular windows are obtained as follows:
/2
2
1
2
( ) ( )
j
jV V
ω
ω
ω
= (6)
( ) ( ) ( )j j jU W Vω ω ω=% % (7)
The algorithmic structure in [8] is then the application of Fourier transform to the original image
to have Fourier coefficients 1 2
ˆ[ , ]f n n , 1 2/ 2 , / 2n n n n− ≤ < . For each scale j and a given
angle l , the product , 1 2 1 2
ˆ[ , ] [ , ]j lU n n f n n% is then computed and wrap to the origin to obtain (8)
4. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.2, April 2013
4
1 2 , 1 2
ˆ[ , ] ( )[ , ]j lf n n W U f n n=% %
(8)
The Algorithm ends by computing the inverse Fourier transform of each ,j lf% to collect Curvelet
coefficients 1 2( , , , )C j l k k .
In [8] are proposed two methods for implementations of Fast Discrete Curvelet Transform
(FDCT): Curvelet via Unequally Spaced Fast Fourier Transform (USFFT) and Curvelet via
Wrapping. We chose to implement FDCT via wrapping in MATLAB environment since this
method has been proved to be faster.
3. ANALYSIS OF CURVELET COEFFICIENTS CONTRIBUTION IN
MICROSCOPIC IMAGES
3.1 Curvelet coefficients contributions
The aim of microscopic images and many medical images is to extract useful information to
underline pathology. For given image of size *m n , we apply the Curvelet transform at different
scales and appreciate the coefficients contributions. Let { }{ } 1 2( , )C j l k k be a Curvelet
coefficient where j is the scale, l the direction parameter and 2
1 2( , )k k k= ∈Z . Given the
decomposition scale j , the coefficients { }{ } 1 21 1 ( , )C k k are the low frequencies contributions
and the other coefficients where 2 j J≤ ≤ are high frequencies contributions. At a scale 2j ≥ ,
we have N orientations such that:
( 2)/2
(2 )j
N Nθ
−
= (9)
Where 2P
Nθ = is orientation number à scale 2j = , ( 2) / 2j − represent the integer part in
excess of ( 2) / 2j − and 3P ≥ .
The Curvelet coefficient contributions in various scales are shown in fig. 3. The original image is
283*275 and can be decomposed at maximal scale 5J = . We can observe that low frequency
scale separate darker region from white on and can be useful for watershed segmentation. We
discover from coarser scales to finer scales, that Curvelet coefficients contributions become
thinner. It is thinner property of thinner scales that will be used to improve edges in microscopic
images.
(a) (b) (c)
5. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.2, April 2013
5
(d) (e) (f)
Figure 3. Curvelet coefficient contributions in each scale: (a) Original image, (b) Curvelet
coefficients contributions at thick scale, (c) Enhanced Curvelet coefficients contributions at scale
2, (d) Enhanced Curvelet coefficients contributions at scale 3, (e) Enhanced Curvelet coefficients
contributions at scale 4, (f) Enhanced Curvelet coefficients contributions at thinner scale.
3.2 Local structure of Curvelet coefficients contributions
The appreciation of a pixel based only on the pixel intensity is not sufficient. Knowing the local
structure of a pixel can be useful to appreciate curvelet coefficients contributions. That is why we
need to find interest points in order to identify robust characteristics in an image. Many interest
points detectors have been presented in literature such as Speeded-Up Robust Features (SURF),
Scale Invariant Feature Transform (SIFT), or Harris Detector [17,19]. We focus on SIFT which is
the most used. SIFT interest point detector is robust to noise and is invariant to scale change.
SIFT algorithm begins by selecting peak in a scale space by calculating minimum and maximum
Difference of Gaussian (DoG) through scales, then follows the elimination of the unstable points
that are points situated in region with low contrast or points situated on contours. Dominant
orientations based on local properties are assigned to interest points to obtain points invariant to
rotation. The SIFT algorithm end by assigning to each key point a 128 element vector that can be
used for mapping. Algorithmic structure use in this paper is from D. Lowe [20].
Fig. 4 shows with cyan colour interest points magnitudes and directions superimpose on the
image. There are 34 interest points in the original image while 61 interest points are found with
scale one Curvelet coefficients contributions. The number of interest points through Curvelet
scales is gathered in table 1.
Table 1: Number of interest points through Curvelet scales.
Curvelet coefficients
contributions at scale:
0 (original) 1 2 3 4 5
Number of SIFT 34 61 17 5 0 0
Number of SIFT interest point
matching original image
34 17 0 0 0 0
It clearly appears that the number and magnitude of interest points globally decreases through
scales. Some few interest points at the finer scale are due to instability of the local structure of
curvelet coefficients contributions and also to the presence of contours.
6. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.2, April 2013
6
(a) (b) (c)
(d) (e) (f)
Figure 4. SIFT Interest points of Curvelet coefficients contribution: (a) SIFT Interest points of the
original image, (b) SIFT Interest points of the Curvelet coefficients contributions at thick scale,
(c) SIFT Interest points of the Curvelet coefficients contributions at scale 2, (d) SIFT Interest
points of the Curvelet coefficients contributions at scale 3, (e) SIFT Interest points of the Curvelet
coefficients contributions at scale 4, (f) SIFT Interest points of the Curvelet coefficients
contributions at thinner scale.
It appears that some few interest points match the original image. These interest points are located
on low frequency curvelet coefficients contributions. Those matching interest points are said to be
stable through scale and could be useful for quick image data base content retrieval or thick blood
smear analysis. Figure 5 present interest points stable through scale.
Figure 5. Interest points stable through scales
4. EDGE IMPROVEMENT USING CURVELET TRANSFORM
On the contrary of authors of [15,16], who use Curvelet transform to improve the quality of
original image before using gradient edge detector to extract contour, we start our algorithm by
contour detection and the Curvelet transform is called upon to improve the contour detected.
Canny edge detector [21] was used in our algorithm to obtain first contours. Then follows
Curvelet transform of original image and contour image at scale J . Thinner scale can be
obtained by increasing the size of the image. The contribution of the Curvelet coefficient of the
7. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.2, April 2013
7
original image at thinner scale then replaces the contribution of the Curvelet coefficient of the
contour image. Finally the inverse Curvelet transform of the contour image is computed. Contour
is obtained by thinning the image obtained after a threshold operation with two times the mean
value. Curvelet transform at scale 1J + brings out more precision on contour.
Figure 6. The diagram of our proposed method
An illustration of Edge improvement algorithm using Curvelet is given by the diagram of figure
6. A test example is shown in figure 7 where (a) is our original image filmed in darkness. (b)
shows contours obtained with canny edge detector. (c) shows improved edges obtained at scale
5J = . This image shows contours of glared light in the downright cell that is not present in
contours obtained with canny edge detector. Moreover there are new cells contour that appear
which are not found on contours from Canny edge detector. (d) depicts improved image obtained
at scale 6J = . This image shows distinct upper left cells contours that are not present in contours
obtained with canny edge detector. After series of test with 10 images, we observe that there is no
contours improvement at scale J lower than 5. We can conclude that thinner scale 4J > can
improve edges. The drawback of our method is its execution time because curvelet transform is
computed twice.
(a) original image (b) Contours from Canny edge
y
n
Canny edge
detection (B)
Curvelet transform
of B at scale J
1J J= +
Curvelet transform
of B at scale J
Original Image (A)
Inverse Curvelet
transform of B
Replacement of thinner scale of
B with thinner scale of A
Thresholding and
thinning
Precision
obtained?
8. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.2, April 2013
8
detector
(c) Improved edge at scale 5J = (d) Improved edge at scale 6J =
Figure 7. Edge improvement using Curvelet: (a) Original image filmed in darkness, (b) Contours
from Canny edge detector, (c) Improved edge at scale 5J = , (d) Improved edge at scale 6J =
5. CONCLUSIONS
This paper presents a method of improvement of contours using the analysis of the coefficients of
Curvelet. Each coefficient contribution at specific scale has information that has not yet been
capitalized enough. Low frequency scale could help for watershed segmentation and it’s SIFT
interest point could facilitate content based image retrieval. It is observed that information on
contour is located on thinner scale. Curvelet transform is a powerful tool for multiresolution
representation of object with anisotropic edge. Experimental results show that thinner scale helps
to improve the edges of the images filmed in the darkness.
REFERENCES
[1] G.T. Shrivakshan, A Comparison of various Edge Detection Techniques used in Image Processing,
IJCSI International Journal of Computer Science Issues, Vol. 9 (5), No 1, September 2012.
[2] R. Maini, H. Aggarwal, Study and Comparison of Various Image Edge Detection Techniques,
International Journal of Image Processing (IJIP), Vol. 3 (1), 2009
[3] JOHN CANNY, A Computational Approach to Edge Detection, IEEE Transactions on Pattern
Analysis and Machine Intelligence, VOL. PAMI-8, NO. 6, NOVEMBER 1986.
[4] Guillaume Joutel, Véronique Eglin, Stéphane Brès, Hubert Emptoz, Extraction de caractéristiques
dans les images par transformée multi-échelle, GRETSI, Troyes. pp. 469-472,
http://liris.cnrs.fr/Documents/Liris-3310.pdf
[5] Stéphane Mallat and Gabriel Peyré, A review of Bandlet methods for geometrical image
representation, Springer Science + Business Media B.V. 2007.
[6] Minh N. Do and Martin Vetterli, The Contourlet Transform: An Efficient Directional Multiresolution
Image Representation, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 14, NO. 12,
DECEMBER 2005.
[7] E. J. Candes and D. L. Donoho, Curvelets a surprisingly effective nonadaptive representation for
objects with edges, pp. 1-10, Vanderbilt University Press, Nashville, TN, 1999.
[8] Emmanuel Candès, Laurent Demanet, David Donoho and Lexing Ying, Fast Discrete Curvelet
Transforms, technical report, California Institute of Technology, 2005.
[9] Aliaa A.A.Youssif, A.A.Darwish, A.M.M. Madbouly, Adaptive Algorithm for Image Denoising
Based on Curvelet Threshold, IJCSNS International Journal of Computer Science and Network
Security, VOL.10 No.1, January 2010.
[10] Muzhir Shaban Al-Ani and Alaa Sulaiman Al-waisy, Face Recognition Approach Based on Wavelet -
Curvelet Technique, Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.2, April
2012, pp 21-31.
9. Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.2, April 2013
9
[11] P. Ashok Babu and K. Prasad , Image Interpolation Using 5/3 Lifting Scheme Approach, Signal &
Image Processing : An International Journal(SIPIJ) Vol.2, No.1, March 2011
[12] Jianwei Ma and Gerlind Plonka, A Review of Curvelets and Recent Applications, IEEE Signal
Processing Magazine, 27 (2), 2010.
[13] Linda Tessens, Aleksandra Pižurica, Alin Alecu, Adrian Munteanu and Wilfried Philips, Context
adaptive image denoising through modeling of curvelet domain statistics, Journal of Electronic
Imaging 17(3), 033021 (Jul–Sep 2008).
[14] Tobias Gebäck and Petros Koumoutsakos, Edge detection in microscopy images using curvelets,
BMC Bioinformatics, 10 (75),1471-2105-10-75 ( 2009).
[15] Zhenghai Liu and Jianxiong Qiu, Study of Technique of Edge Detection Based on Curvelet
Transform, IEEE computer society, Proc. Second International Symposium on Computational
Intelligence and Design 2009, pp 543-545.
[16] Yin-Cheng Qi, Xiao-Shuang Xing and Hua-Fang-Zi Zhang, Blind Detection of Eclosion Forgeries
Based on Curvelet Image Enhancement and Edge Detection, IEEE computer society, Proc.
International Conference on Multimedia and Signal Processing 2011, pp 316–320.
[17] D. Lowe, Distinctive Image Features From Scale-Invariant Keypoints, International Journal of
Computer Vision, Vol. 60 (2), November 2004, pp 91 – 110.
[18] Qin Xue, Zhuangzhi Yan and Shengqian Wang, Mammographic Feature Enhancement Based on
Second Generation Curvelet Transform, 3rd International Conference on Biomedical Engineering and
Informatics (BMEI), Vol. 1, 16-18 Oct. 2010, pp 349 – 353.
[19] Steffen Gauglitz, Tobias Höllerer and Matthew Turk, Evaluation of Interest Point Detectors and
Feature Descriptors for Visual Tracking, International Journal of Computer Vision, DOI
10.1007/s11263-011-0431-5, Vol. 94 (3), September 2011, pp 335-360 .
[20] http://www.cs.ubc.ca/~lowe/keypoints/siftDemoV4.zip
[21] T. Luo, X. Zheng, and T. Ding, Improved self-adaptive threshold canny edge detection, Opto-
electronic Engineering, Vol. 36, No. 11, 2009.