This paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. By combining the discriminating power of our image descriptor and classifier, our system uses small size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz dataset. High classification rates demonstrate the superiority of the proposed method. © Springer-Verlag Berlin Heidelberg 2007.Trabajo de investigació
International audienceIn this paper, we present rotation invariant descriptors using regional rank f...
This letter introduces a novel approach to rotation and scale invariant texture classification. The ...
In this paper, we present a new rotation-invariant tex-ture descriptor algorithm called Invariant Fe...
Abstract. This paper proposes a new texture classification system, which is distinguished by: (1) a ...
Learning how to extract texture features from noncontrolled environments characterized by distorted ...
A rotation-invariant texture recognition system is presented. A steerable oriented pyramid is used t...
A rotation-invariant texture recognition system is presented. A steerable oriented pyramid is used t...
This paper proposes a new rotation-invariant and scale-invariant representation for texture image re...
In this paper, we present a theoretically and computationally simple but efficient approach for rota...
This paper constructs a texture feature extractor based on a morphological scale-space. It produces ...
A method of rotation invariant texture classification based on spatial frequency model is developed....
In this thesis, we develop a feature extraction technique for texture classification and Rotation In...
This paper proposes a new rotation-invariant and scale-invariant representation for texture image re...
This paper proposes a set of efficient algorithms for rotation- and scale-invariant texture classifi...
In this paper, a model based texture classification procedure is presented. The texture is modeled a...
International audienceIn this paper, we present rotation invariant descriptors using regional rank f...
This letter introduces a novel approach to rotation and scale invariant texture classification. The ...
In this paper, we present a new rotation-invariant tex-ture descriptor algorithm called Invariant Fe...
Abstract. This paper proposes a new texture classification system, which is distinguished by: (1) a ...
Learning how to extract texture features from noncontrolled environments characterized by distorted ...
A rotation-invariant texture recognition system is presented. A steerable oriented pyramid is used t...
A rotation-invariant texture recognition system is presented. A steerable oriented pyramid is used t...
This paper proposes a new rotation-invariant and scale-invariant representation for texture image re...
In this paper, we present a theoretically and computationally simple but efficient approach for rota...
This paper constructs a texture feature extractor based on a morphological scale-space. It produces ...
A method of rotation invariant texture classification based on spatial frequency model is developed....
In this thesis, we develop a feature extraction technique for texture classification and Rotation In...
This paper proposes a new rotation-invariant and scale-invariant representation for texture image re...
This paper proposes a set of efficient algorithms for rotation- and scale-invariant texture classifi...
In this paper, a model based texture classification procedure is presented. The texture is modeled a...
International audienceIn this paper, we present rotation invariant descriptors using regional rank f...
This letter introduces a novel approach to rotation and scale invariant texture classification. The ...
In this paper, we present a new rotation-invariant tex-ture descriptor algorithm called Invariant Fe...