Texture classification using the cortex transform

Abstract Texture classification has been the subject of extensive research during the last several years. The key step in any texture classification process is the choice of the set of features to be used to characterize the texture. In this paper, the strategy for developing a classification system is to consider texture classification as performed by the human visual system; therefore the feature extraction stage is based on the cortex transform. A new and simple algorithm for implementing the cortex transform is developed; a texture classification system based on it is evaluated. The initial classification results are promising and appear to be robust with respect to a noisy environment.

[1]  Anthony N. Mucciardi,et al.  A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties , 1971, IEEE Transactions on Computers.

[2]  R. L. Valois,et al.  The orientation and direction selectivity of cells in macaque visual cortex , 1982, Vision Research.

[3]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[4]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

[5]  J. Movshon,et al.  Spatial summation in the receptive fields of simple cells in the cat's striate cortex. , 1978, The Journal of physiology.

[6]  D G Stork,et al.  Do Gabor functions provide appropriate descriptions of visual cortical receptive fields? , 1990, Journal of the Optical Society of America. A, Optics and image science.

[7]  B. Eisenstein,et al.  Feature selection via dynamic programming for text-independent speaker identification , 1978 .

[8]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[9]  T. Martin,et al.  On the effects of varying filter bank parameters on isolated word recognition , 1982 .

[10]  P. O. Bishop,et al.  Orientation, axis and direction as stimulus parameters for striate cells. , 1974, Vision research.

[11]  B. S. Rubenstein,et al.  Spatial variability as a limiting factor in texture-discrimination tasks: implications for performance asymmetries. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[12]  D. Pollen,et al.  Relationship between spatial frequency selectivity and receptive field profile of simple cells. , 1979, The Journal of physiology.

[13]  S Marcelja,et al.  Mathematical description of the responses of simple cortical cells. , 1980, Journal of the Optical Society of America.

[14]  Andrew B. Watson,et al.  The cortex transform: rapid computation of simulated neural images , 1987 .

[15]  M. Sambur,et al.  Selection of acoustic features for speaker identification , 1975 .

[16]  Aaron E. Rosenberg,et al.  Speaker-independent recognition of isolated words using clustering techniques , 1979 .

[17]  Robert King,et al.  Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..

[18]  M. E. Jernigan,et al.  Texture Analysis and Discrimination in Additive Noise , 1990, Comput. Vis. Graph. Image Process..

[19]  D. M. MacKay,et al.  Strife over visual cortical function , 1981, Nature.

[20]  L. Maffei,et al.  The visual cortex as a spatial frequency analyser. , 1973, Vision research.

[21]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[22]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[23]  A B Watson,et al.  Efficiency of a model human image code. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[24]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[25]  R. Mansfield,et al.  Neural Basis of Orientation Perception in Primate Vision , 1974, Science.

[26]  M. E. Jernigan,et al.  Entropy-Based Texture Analysis in the Spatial Frequency Domain , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  D. Pollen,et al.  Phase relationships between adjacent simple cells in the visual cortex. , 1981, Science.

[28]  M. Porat,et al.  Localized texture processing in vision: analysis and synthesis in the Gaborian space , 1989, IEEE Transactions on Biomedical Engineering.

[29]  G. F. Cooper,et al.  The spatial selectivity of the visual cells of the cat , 1969, The Journal of physiology.

[30]  P Artal,et al.  Incorporation of directional effects of the retina into computations of optical transfer functions of human eyes. , 1989, Journal of the Optical Society of America. A, Optics and image science.

[31]  Harry Wechsler,et al.  Texture analysis — a survey , 1980 .

[32]  L. Rabiner,et al.  A simplified, robust training procedure for speaker trained, isolated word recognition systems , 1980 .