Supervised Texture Classification Using Characteristic Generalized Gaussian Density
Journal of Mathematical Imaging and Vision
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Pattern Recognition Letters
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Journal of Mathematical Imaging and Vision
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Computer Vision and Image Understanding
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SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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IEEE Transactions on Image Processing
Region-Based Active Contours with Exponential Family Observations
Journal of Mathematical Imaging and Vision
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SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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Texture classification using refined histogram
IEEE Transactions on Image Processing
Variational region-based segmentation using multiple texture statistics
IEEE Transactions on Image Processing
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
An active contour model guided by LBP distributions
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Deformable-Model based textured object segmentation
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Combined geometric-texture image classification
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Journal of Biomedical Imaging - Special issue on Mathematical Methods for Images and Surfaces 2011
Bimodal texture segmentation with the Lee-Seo model
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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We present a supervised classification model based on a variational approach. This model is specifically devoted to textured images. We want to get a partition of an image, composed of texture regions separated by regular interfaces. Each kind of texture defines a class. We use a wavelet packet transform to analyze the textures, characterized by their energy distribution in each sub-band. In order to have an image segmentation according to the classes, we model the regions and their interfaces by level set functions. We define a functional on these level sets whose minimizers define the optimal classification according to texture. A system of coupled PDEs is deduced from the functional. By solving this system, each region evolves according to its wavelet coefficients and interacts with the neighbor regions in order to obtain a partition with regular contours. Experiments are shown on synthetic and real images.