International audienceThis paper decribes a new probabilistic framework for recognizing textures in images. Images are described by local affine-invariant descriptors and by spatial relationships between these descriptors. We propose to introduce the use of statistical parametric models of the dependence between descriptors. Hidden Markov Models (HMM) are investigated for such a task using recent estimation procedures based on the mean eld principle to perform the non trivial parameter estimation they require. Preliminary experiments obtained with 140 images of seven dierent natural textures show promising results
The general problem of unsupervised textured image segmentation remains a fundamental but not entire...
Texture analysis is one of the key techniques of image understanding and processing with widespread ...
The general problem of unsupervised textured image segmentation remains a fundamental but not entire...
International audienceThis paper describes a new probabilistic framework for recognizing textures in...
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 audienceWe present a framework for texture recognition based on local affine-invariant...
This paper proposes, applies and evaluates a new technique for texture classification in digital ima...
[[abstract]]Texture features obtained by fitting generalized Ising, auto-binomial, and Gaussian Mark...
In this paper we present noncausal, nonparametric, multiscale, Markov Random Field (MRF) model for s...
Abstract. This article presents a statistical theory for texture modeling. This theory combines filt...
In this paper we present noncausal, nonparametric, multiscale, Markov Random Field (MRF) models for ...
This paper proposes a novel robust texture descriptor based on Gaussian Markov random fields (GMRFs)...
[[abstract]]Texture classification systems are characterized, existing techniques for texture classi...
The underlying aim of this research is to investigate the mathematical descriptions of homogeneous t...
The general problem of unsupervised textured image segmentation remains a fundamental but not entire...
Texture analysis is one of the key techniques of image understanding and processing with widespread ...
The general problem of unsupervised textured image segmentation remains a fundamental but not entire...
International audienceThis paper describes a new probabilistic framework for recognizing textures in...
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 audienceWe present a framework for texture recognition based on local affine-invariant...
This paper proposes, applies and evaluates a new technique for texture classification in digital ima...
[[abstract]]Texture features obtained by fitting generalized Ising, auto-binomial, and Gaussian Mark...
In this paper we present noncausal, nonparametric, multiscale, Markov Random Field (MRF) model for s...
Abstract. This article presents a statistical theory for texture modeling. This theory combines filt...
In this paper we present noncausal, nonparametric, multiscale, Markov Random Field (MRF) models for ...
This paper proposes a novel robust texture descriptor based on Gaussian Markov random fields (GMRFs)...
[[abstract]]Texture classification systems are characterized, existing techniques for texture classi...
The underlying aim of this research is to investigate the mathematical descriptions of homogeneous t...
The general problem of unsupervised textured image segmentation remains a fundamental but not entire...
Texture analysis is one of the key techniques of image understanding and processing with widespread ...
The general problem of unsupervised textured image segmentation remains a fundamental but not entire...