This paper constructs a texture feature extractor based on a morphological scale-space. It produces features that are invariant to rotation of the texture. The features are used with a very simple k-nearest neighbour classifier and tested using the Outex methodology. The classifier has comparable performance to a number of benchmark classifiers but uses fewer features. The algorithm is quick to compute and provides insight into the underlying structure of texture
In this paper we propose a new rotation invariant feature descriptor for texture classification and ...
In this paper we describe a rotation-invariant multi-scale morphological method for texture analysis...
A novel rotation and scale invariant texture classification methodologyis proposed based on distribu...
Learning how to extract texture features from noncontrolled environments characterized by distorted ...
International audienceIn this paper, we present rotation invariant descriptors using regional rank f...
This paper proposes a new texture classification system, which is distinguished by: (1) a new rotati...
Abstract. This paper proposes a new texture classification system, which is distinguished by: (1) a ...
In this paper, we present a theoretically and computationally simple but efficient approach for rota...
In this paper, we present a new rotation-invariant tex-ture descriptor algorithm called Invariant Fe...
This paper proposes a set of efficient algorithms for rotation- and scale-invariant texture classifi...
A rotation-invariant texture recognition system is presented. A steerable oriented pyramid is used t...
A method of rotation invariant texture classification based on spatial frequency model is developed....
In this paper, a model based texture classification procedure is presented. The texture is modeled a...
In content-based image retrieval systems, the texture in the query image may appear at a different s...
In this paper, we introduce a rotational invariant feature set for texture classification, based on ...
In this paper we propose a new rotation invariant feature descriptor for texture classification and ...
In this paper we describe a rotation-invariant multi-scale morphological method for texture analysis...
A novel rotation and scale invariant texture classification methodologyis proposed based on distribu...
Learning how to extract texture features from noncontrolled environments characterized by distorted ...
International audienceIn this paper, we present rotation invariant descriptors using regional rank f...
This paper proposes a new texture classification system, which is distinguished by: (1) a new rotati...
Abstract. This paper proposes a new texture classification system, which is distinguished by: (1) a ...
In this paper, we present a theoretically and computationally simple but efficient approach for rota...
In this paper, we present a new rotation-invariant tex-ture descriptor algorithm called Invariant Fe...
This paper proposes a set of efficient algorithms for rotation- and scale-invariant texture classifi...
A rotation-invariant texture recognition system is presented. A steerable oriented pyramid is used t...
A method of rotation invariant texture classification based on spatial frequency model is developed....
In this paper, a model based texture classification procedure is presented. The texture is modeled a...
In content-based image retrieval systems, the texture in the query image may appear at a different s...
In this paper, we introduce a rotational invariant feature set for texture classification, based on ...
In this paper we propose a new rotation invariant feature descriptor for texture classification and ...
In this paper we describe a rotation-invariant multi-scale morphological method for texture analysis...
A novel rotation and scale invariant texture classification methodologyis proposed based on distribu...