International audienceThis paper describes a new probabilistic framework for recognizing textures in images. Images are described by local affine-invariant descriptors and their spatial relationships. We introduce a statistical parametric models of the dependence between descriptors. We use Hidden Markov Models (HMM) and estimate the parameters with a recent technique based on the mean field principle. Preliminary results for texture recognition are promising and outperform existing techniques
The Bayesian approach to image processing based on Markov random fields is adapted to image analysis...
The Bayesian approach to image processing based on Markov random fields is adapted to image analysis...
The Bayesian approach to image processing based on Markov random fields is adapted to image analysis...
International audienceThis paper decribes a new probabilistic framework for recognizing textures in ...
International audienceThis paper describes a new probabilistic framework for recognizing textures in...
International audienceThis paper decribes a new probabilistic framework for recognizing textures in ...
This paper proposes a novel robust texture descriptor based on Gaussian Markov random fields (GMRFs)...
In this paper we present noncausal, nonparametric, multiscale, Markov Random Field (MRF) model for s...
[[abstract]]Texture features obtained by fitting generalized Ising, auto-binomial, and Gaussian Mark...
International audienceWe present a framework for texture recognition based on local affine-invariant...
Abstract—A visual appearance of natural materials funda-mentally depends on illumination conditions,...
In this paper we present noncausal, nonparametric, multiscale, Markov Random Field (MRF) models for ...
The general problem of unsupervised textured image segmentation remains a fundamental but not entire...
The general problem of unsupervised textured image segmentation remains a fundamental but not entire...
This paper proposes, applies and evaluates a new technique for texture classification in digital ima...
The Bayesian approach to image processing based on Markov random fields is adapted to image analysis...
The Bayesian approach to image processing based on Markov random fields is adapted to image analysis...
The Bayesian approach to image processing based on Markov random fields is adapted to image analysis...
International audienceThis paper decribes a new probabilistic framework for recognizing textures in ...
International audienceThis paper describes a new probabilistic framework for recognizing textures in...
International audienceThis paper decribes a new probabilistic framework for recognizing textures in ...
This paper proposes a novel robust texture descriptor based on Gaussian Markov random fields (GMRFs)...
In this paper we present noncausal, nonparametric, multiscale, Markov Random Field (MRF) model for s...
[[abstract]]Texture features obtained by fitting generalized Ising, auto-binomial, and Gaussian Mark...
International audienceWe present a framework for texture recognition based on local affine-invariant...
Abstract—A visual appearance of natural materials funda-mentally depends on illumination conditions,...
In this paper we present noncausal, nonparametric, multiscale, Markov Random Field (MRF) models for ...
The general problem of unsupervised textured image segmentation remains a fundamental but not entire...
The general problem of unsupervised textured image segmentation remains a fundamental but not entire...
This paper proposes, applies and evaluates a new technique for texture classification in digital ima...
The Bayesian approach to image processing based on Markov random fields is adapted to image analysis...
The Bayesian approach to image processing based on Markov random fields is adapted to image analysis...
The Bayesian approach to image processing based on Markov random fields is adapted to image analysis...