Generalized texture representation and metric

Abstract A new approach to texture representation and discrimination is proposed. The representation is devised to reflect various perceptual aspects of image texture by retaining as much as possible the extracted texture information. Information thus extracted is then organized as a set of line or circular frequency diagrams for various texture features at different resolution levels. Based upon the properties of the two types of frequency diagrams, new metrics are defined to enhance the feature dissimilarities. The effectiveness of the proposed representation and metric for texture analysis is also illustrated by a set of actual data.

[1]  S. Zucker.  Toward a model of texture , 1976 .

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

[3]  Ernest L. Hall,et al.  Texture Measures for Automatic Classification of Pulmonary Disease , 1972, IEEE Transactions on Computers.

[4]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Béla Julesz,et al.  Visual Pattern Discrimination , 1962, IRE Trans. Inf. Theory.

[6]  Andrew K. C. Wong,et al.  Resolution-Dependent Information Measures for Image Analysis , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

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

[8]  J. Modestino,et al.  Texture discrimination based upon an assumed stochastic texture model , 1979, 1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

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

[10]  D Marr,et al.  Early processing of visual information. , 1976, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[11]  B Julesz,et al.  Experiments in the visual perception of texture. , 1975, Scientific American.

[12]  Luigia Carlucci,et al.  A formal system for texture languages , 1972, Pattern Recognit..

[13]  William B. Thompson,et al.  Computer Diagnosis of Pneumoconiosis , 1974, IEEE Trans. Syst. Man Cybern..

[14]  George G. Lendaris,et al.  Diffraction-pattern sampling for automatic pattern recognition , 1970 .

[15]  Azriel Rosenfeld,et al.  Digital Picture Processing , 1976 .

[16]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[17]  A. Rosenfeld,et al.  Edge and Curve Detection for Visual Scene Analysis , 1971, IEEE Transactions on Computers.

[18]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  King-Sun Fu,et al.  Stochastic tree grammar inference for texture synthesis and discrimination , 1979 .

[20]  Larry S. Davis,et al.  Texture Analysis Using Generalized Co-Occurrence Matrices , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Larry S. Davis,et al.  Polarograms: A new tool for image texture analysis , 1979, Pattern Recognit..

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

[23]  R. Haralick,et al.  Computer Classification of Reservoir Sandstones , 1973 .

[24]  R. Ehrich,et al.  A view of texture topology and texture description , 1978 .

[25]  King-Sun Fu,et al.  A syntactic approach to texture analysis , 1978 .