The Experts below are selected from a list of 23289 Experts worldwide ranked by ideXlab platform
Rachid Deriche - One of the best experts on this subject based on the ideXlab platform.
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active unsupervised Texture Segmentation on a diffusion based feature space
Computer Vision and Pattern Recognition, 2003Co-Authors: Mikaël Rousson, Thomas Brox, Rachid DericheAbstract:We propose a novel and efficient approach for active unsupervised Texture Segmentation. First, we show how we can extract a small set of good features for Texture Segmentation based on the structure tensor and nonlinear diffusion. Then, we propose a variational framework that incorporates these features in a level set based unsupervised Segmentation process that adaptively takes into account their estimated statistical information inside and outside the region to segment. The approach has been tested on various Textured images, and its performance is favorably compared to recent studies.
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Active Unsupervised Texture Segmentation on a Diffusion Based Feature Space
2003Co-Authors: Mikaël Rousson, Thomas Brox, Rachid DericheAbstract:In this report, we propose a novel and efficient approach for active unsurpervised Texture Segmentation. First, we show how we can extract a small set of good features for Texture Segmentation based on the structure tensor and nonlinear diffusion. Then, we propose a variational framework that allows to incorporate these features in a level set based unsupervised Segmentation process that adaptively takes into account their estimated statistical information inside and outside the region to segment. Unlike features obtained by Gabor filters, our approach naturally leads to a significantly reduced number of feature channels. Thus, the supervised part of a Texture Segmentation algorithm, where the choice of good feature channels has to be learned in advance, can be omitted, and we get an efficient solution for unsupervised Texture Segmentation. The actual Segmentation process based on the new features is an active and adaptative contour model that estimates dynamically probability density functions inside and outside a region and produces very convincing results. It is implemented using a fast level set based active contour technique and has been tested on various real Textured images. The performance of the approach is favorably compared to recent studies.
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geodesic active regions and level set methods for supervised Texture Segmentation
International Journal of Computer Vision, 2002Co-Authors: Nikos Paragios, Rachid DericheAbstract:This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and region-based Segmentation modules under a curve-based optimization objective function. The task of supervised Texture Segmentation is considered to demonstrate the potentials of the proposed framework. The Textured feature space is generated by filtering the given Textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The Texture Segmentation is obtained by unifying region and boundary-based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region-based Segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real Textured frames.
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geodesic active regions for supervised Texture Segmentation
International Conference on Computer Vision, 1999Co-Authors: Nikos Paragios, Rachid DericheAbstract:The paper presents a novel variational method for supervised Texture Segmentation. The Textured feature space is generated by filtering the given Textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The Texture Segmentation is obtained by unifying region and boundary based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region based Segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real Textured frames.
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geodesic active contours for supervised Texture Segmentation
Computer Vision and Pattern Recognition, 1999Co-Authors: N Paraagios, Rachid DericheAbstract:This paper presents a variational method for supervised Texture Segmentation which is based on ideas coming from the curve propagation theory. We assume that a preferable Texture pattern is known (e.g., the pattern that we want to distinguish from the rest of the image). The Textured feature space is generated by filtering the input and the preferable pattern image using Gabor filters, and analyzing their responses as multi-component conditional probability density functions. The Texture Segmentation is obtained by minimizing a Geodesic Active Contour Model objective function where the boundary-based information is expressed via discontinuities on the statistical space associated with the multi-modal Textured feature space. This function is minimized using a gradient descent method where the obtained PDE is implemented using a level set approach, that handles naturally the topological changes. Finally a fast method is used for the level set implementation. The performance of our method is demonstrated on a variety of synthetic and real Textured images.
R Chellappa - One of the best experts on this subject based on the ideXlab platform.
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multiresolution gauss markov random field models for Texture Segmentation
IEEE Transactions on Image Processing, 1997Co-Authors: S Krishnamachari, R ChellappaAbstract:This paper presents multiresolution models for Gauss-Markov random fields (GMRFs) with applications to Texture Segmentation. Coarser resolution sample fields are obtained by subsampling the sample field at fine resolution. Although the Markov property is lost under such resolution transformation, coarse resolution non-Markov random fields can be effectively approximated by Markov fields. We present two techniques to estimate the GMRF parameters at coarser resolutions from the fine resolution parameters, one by minimizing the Kullback-Leibler distance and another based on local conditional distribution invariance. We also allude to the fact that different GMRF parameters at the fine resolution can result in the same probability measure after subsampling and present the results for first- and second-order cases. We apply this multiresolution model to Texture Segmentation. Different Texture regions in an image are modeled by GMRFs and the associated parameters are assumed to be known. Parameters at lower resolutions are estimated from the fine resolution parameters. The coarsest resolution data is first segmented and the Segmentation results are propagated upward to the finer resolution. We use the iterated conditional mode (ICM) minimization at all resolutions. Our experiments with synthetic, Brodatz Texture, and real satellite images show that the multiresolution technique results in a better Segmentation and requires lesser computation than the single resolution algorithm.
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unsupervised Texture Segmentation using markov random field models
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991Co-Authors: B S Manjunath, R ChellappaAbstract:The problem of unsupervised Segmentation of Textured images is considered. The only explicit assumption made is that the intensity data can be modeled by a Gauss Markov random field (GMRF). The image is divided into a number of nonoverlapping regions and the GMRF parameters are computed from each of these regions. A simple clustering method is used to merge these regions. The parameters of the model estimated from the clustered segments are then used in two different schemes, one being all approximation to the maximum a posterior estimate of the labels and the other minimizing the percentage misclassification error. The proposed approach is contrasted with the algorithm of S. Lakshamanan and H. Derin (1989), which uses a simultaneous parameter estimation and Segmentation scheme. The results of the adaptive Segmentation algorithm of Lakshamanan and Derin are compared with a simple nearest-neighbor classification scheme to show that if enough information is available, simple techniques could be used as alternatives to computationally expensive schemes. >
Patrice Abry - One of the best experts on this subject based on the ideXlab platform.
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Scale-free Texture Segmentation: Expert Feature-based versus Deep Learning strategies
2021Co-Authors: Barbara Pascal, Nelly Pustelnik, Vincent Mauduit, Patrice AbryAbstract:Texture Segmentation constitutes a central task in image processing, classically based on two-step procedures consisting first in computing hand-crafted features devised from a priori expert knowledge and second in combining them into clustering algorithms. Deep learning approaches can be seen as merging these two steps into a single one with both discovering features and performing Segmentation. Using fractal Textures, often seen as relevant models in real-world applications, the present work compares a recently devised Texture Segmentation algorithm incorporating expert-driven scale-free features estimation into a Joint TV optimization framework against convolutional neural network architectures. From realistic synthetic Textures, comparisons are drawn not only for Segmentation performance, but also with respect to computational costs, architecture complexities and robustness against departures between training and testing datasets.
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how joint fractal features estimation and Texture Segmentation can be cast into a strongly convex optimization problem
arXiv: Optimization and Control, 2019Co-Authors: Barbara Pascal, Nelly Pustelnik, Patrice AbryAbstract:The present work investigates the Segmentation of Textures by formulating it as a strongly convex optimization problem, aiming to favor piecewise constancy of fractal features (local variance and local regularity) widely used to model real-world Textures in numerous applications very different in nature. Two objective functions combining these two features are compared, referred to as joint and coupled, promoting either independent or co-localized changes in local variance and regularity. To solve the resulting convex nonsmooth optimization problems, because the processing of large size images and databases are targeted, two categories of proximal algorithms (dual forward-backward and primal-dual), are devised and compared. An in-depth study of the objective functions, notably of their strong convexity, memory and computational costs, permits to propose significantly accelerated algorithms. A class of synthetic models of piecewise fractal Texture is constructed and studied. They enable, by means of large size Monte Carlo simulations, to quantify the benefits in Texture Segmentation of using together local regularity and local variance (as opposed to regularity only) as well as of using strong-convexity accelerated primal-dual algorithms. Achieved results also permit to discuss the gains/costs in imposing or not in the problem formulation co-localizations of changes in local regularity and local variance. Finally, the potential of the proposed approaches is illustrated on real-world Textures taken from a publicly available and documented database.
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Joint estimation of local variance and local regularity for Texture Segmentation. Application to multiphase flow characterization.
2018Co-Authors: Barbara Pascal, Nelly Pustelnik, Patrice Abry, Marion Serres, Valérie VidalAbstract:Texture Segmentation constitutes a task of utmost importance in statistical image processing. Focusing on the broad class of monofrac-tal Textures characterized by piecewise constancy of the statistics of their multiscale representations, recently shown to be versatile enough for real-world Texture modeling, the present work renews this recurrent topic by proposing an original approach enrolling jointly scale-free and local variance descriptors into a convex, but non smooth, minimization strategy. The performance of the proposed joint approach are compared against disjoint strategies working independently on scale-free features and on local variance on synthetic piecewise monofractal Textures. Performance are also compared for multiphase flow image characterization, a topic of crucial importance in geophysics as well as in industrial processes. Applied to large-size images (above two million pixels), the proposed approach is shown to significantly improve state-of-the-art strategies by permitting the detection of the smallest gas bubbles and by offering a better understanding of multiphase flow structures.
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Block-coordinate proximal algorithms for scale-free Texture Segmentation
2018Co-Authors: Barbara Pascal, Nelly Pustelnik, Patrice Abry, Jean-christophe PesquetAbstract:Texture Segmentation still constitutes an ongoing challenge, especially when processing large-size images. Recently, procedures integrating a scale-free (or fractal) wavelet-leader model allowed the problem to be reformulated in a convex optimization framework by including a TV penalization. In this case, the TV penalty plays a prominent role with respect to the data fidelity term, which makes the approach costly in terms of memory and computation cost. The present contribution aims to investigate the potential of recent block-coordinate dual and primal-dual proximal algorithms for overcoming this numerical issue. Our study shows that a key ingredient in the success of the proposed block-coordinate approaches lies in the design of the blocks of variables which are updated at each iteration. Numerical experiments conducted over synthetic Textures having piece-wise constant fractal properties confirm our theoretical analysis. The proposed lattice block design strategy is shown to yield significantly lower memory and computational requirements.
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IVMSP - Multifractal-based Texture Segmentation using variational procedure
2016 IEEE 12th Image Video and Multidimensional Signal Processing Workshop (IVMSP), 2016Co-Authors: Jordan Frecon, Nelly Pustelnik, Herwig Wendt, Laurent Condat, Patrice AbryAbstract:The present contribution aims at segmenting a scale-free Texture into different regions, characterized by an a priori (unknown) multifractal spectrum. The multifractal properties are quantified using multiscale quantities C1, j and C2, j that quantify the evolution along the analysis scales 2j of the empirical mean and variance of a nonlinear transform of wavelet coefficients. The Segmentation is performed jointly across all the scales j on the concatenation of both C1, j and C2, j by an efficient vectorial extension of a convex relaxation of the piecewise constant Potts Segmentation problem. We provide comparisons with the scalar Segmentation of the Holder exponent as well as independent vectorial Segmentations over C1 and C2.
Fabrice Heitz - One of the best experts on this subject based on the ideXlab platform.
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a markov random field model based approach to unsupervised Texture Segmentation using local and global spatial statistics
IEEE Transactions on Image Processing, 1995Co-Authors: Charles Kervrann, Fabrice HeitzAbstract:Many studies have proven that statistical model-based Texture Segmentation algorithms yield good results provided that the model parameters and the number of regions be known a priori. In this correspondence, we present an unsupervised Texture Segmentation method that does not require knowledge about the different Texture regions, their parameters, or the number of available Texture classes. The proposed algorithm relies on the analysis of local and global second and higher order spatial statistics of the original images. The Segmentation map is modeled using an augmented-state Markov random field, including an outlier class that enables dynamic creation of new regions during the optimization process. A Bayesian estimate of this map is computed using a deterministic relaxation algorithm. Results on real-world Textured images are presented. >
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A markov random field model-based approach to unsupervised Texture Segmentation using local and global spatial statistics
1993Co-Authors: Charles Kervrann, Fabrice HeitzAbstract:The general problem of unsupervised Textured image Segmentation remains a fundamental but not entirely solved issue in image analysis. Many studies have proven that statistical model-based Texture Segmentation algorithms yield good results provided that the model parameters and the number of regions be known a priori. In this paper, we present an unsupervised Texture Segmentation method which does not require a priori knowledge about the different Texture regions, their parameters or the number of available Texture classes. The proposed algorithm relies on the analysis of local and global second and higher order spatial statistics of the original images. The Segmentation map is modeled using an augmented-state Markov random field, including an outlier class which enables dynamic creation of new regions during the optimization process. A bayesian estimates of this map is computed using a deterministic relaxation algorithm. The whole Segmentation procedure is controlled by one single parameter. Results on mosaics of natural Textures and real-world Textured images show the ability of the model to yield relevant and robust Segmentations when the number of regions and the different Texture classes are not known a priori.
Ping Guo - One of the best experts on this subject based on the ideXlab platform.
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Texture Segmentation based on neuronal activation degree of visual model
International Conference on Neural Information Processing, 2012Co-Authors: Fuqing Duan, Ping GuoAbstract:In the study of object recognition, image Texture Segmentation has being a hot and difficult aspect in computer vision. Feature extraction and Texture Segmentation algorithm are two key steps in Texture Segmentation. An effective Texture description is the important factor of Texture Segmentation. In this paper, the neuronal activation degree (NAD) of visual model is exploited as the Texture description of image patches. By processing the length and direction of NAD, we develop an effective Segmentation strategy. First, the length of the NAD are used to partition blank area and non-blank area, then the mark index of neuron is used, which is maximally activated to identify the label of each segment unit to get an initial Segmentation. Finally, region merging steps is exerted to get a desired result.
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ICONIP (5) - Texture Segmentation based on neuronal activation degree of visual model
Neural Information Processing, 2012Co-Authors: Fuqing Duan, Ping GuoAbstract:In the study of object recognition, image Texture Segmentation has being a hot and difficult aspect in computer vision. Feature extraction and Texture Segmentation algorithm are two key steps in Texture Segmentation. An effective Texture description is the important factor of Texture Segmentation. In this paper, the neuronal activation degree (NAD) of visual model is exploited as the Texture description of image patches. By processing the length and direction of NAD, we develop an effective Segmentation strategy. First, the length of the NAD are used to partition blank area and non-blank area, then the mark index of neuron is used, which is maximally activated to identify the label of each segment unit to get an initial Segmentation. Finally, region merging steps is exerted to get a desired result.
