Texture Analysis Using Rényi’s Generalized Entropies

We propose a texture analysis method based on Rényi’s generalized entropies. The method aims at identifying texels in regular textures by searching for the smallest window through which the minimum number of different visual patterns is observed when moving the window over a given texture. The results show that any of Rényi’s entropies can be used for texel identification. However, the second order entropy, due to its robust estimation, is the most reliable. The main advantages of the proposed method are its robustness and its flexibility. We illustrate the usefulness and the effectiveness of the method in a texture synthesis application.

A dynamical system approach to texel identification in regular textures

We propose a texture analysis method based on Rényi’s entropies. The method aims at identifying texels in regular textures by searching for the smallest window through which the minimum number of different visual patterns is observed when moving the window over a given texture. The experimental results show that any of Rényi’s entropies can be used for texel identification. However, the second order entropy, due to its robust estimation, is the most reliable. The main advantages of the proposed method are its robustness and its flexibility. We illustrate the usefulness and the effectiveness of the method in a texture synthesis application and we compare it with other structural approaches.

A comparative study of filter based texture operators using Mahalanobis distance

Texture feature extraction operators, which comprise linear filtering, eventually followed by post-processing, are considered. The filters used are Laws’ masks, filters derived from well-known discrete transforms, and Gabor filters. The post-processing step comprises non-linear point operations and/or local statistics computation. The performance is measured by means of the Mahalanobis distance between clusters of feature vectors derived from different textures. The results show that post-processing improve considerably the performance of filter based texture operators.

Comparison of texture features based on Gabor filters

The performance of a number of texture feature operators is evaluated. The features are all based on the local spectrum which is obtained by a bank of Gabor filters. The comparison is made using a quantitative method which is based on Fisher’s criterion. It is shown that, in general, the discrimination effectiveness of the features increases with the amount of post-Gabor processing.