The amplitude varying rate statistical approach for texture classification

Abstract The Amplitude Varying Rate Statistical Approach (AVRS) for texture analysis is based on estimating the distribution of the size of features given n intensity threshold. Experiments with Brodatz textures gave 92% accuracy with a nearest neighbor classifier and 85% accuracy with a minimum distance classifier.

[1]  C. K. Yuen,et al.  Walsh Functions and Their Applications , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Kenneth I. Laws,et al.  Goal-Directed Textured-Image Segmentation , 1985, Other Conferences.

[3]  Makoto Nagao,et al.  A structural analyzer for regularly arranged textures , 1982, Comput. Graph. Image Process..

[4]  Azriel Rosenfeld,et al.  Relative Effectiveness of Selected Texture Primitive Statistics for Texture Discrimination , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Richard W. Conners,et al.  Toward a Structural Textural Analyzer Based on Statistical Methods , 1980 .

[6]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

[7]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[8]  Henning F. Harmuth,et al.  Transmission of information by orthogonal functions , 1969 .

[9]  Ramakant Nevatia,et al.  Structural Analysis of Natural Textures , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Larry S. Davis Computing the spatial structures of cellular textures , 1979 .

[11]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[12]  Larry S. Davis,et al.  Texture classification by local rank correlation , 1985, Comput. Vis. Graph. Image Process..

[13]  Luc Van Gool,et al.  Texture analysis Anno 1983 , 1985, Comput. Vis. Graph. Image Process..