Classification of natural textures by means of two-dimensional orthogonal masks

An approach to texture classification in which each local neighborhood is transformed reversibly into a minimal set of uncorrelated features is described. These features are found to represent local average, as well as first- and second-order derivative information of the texture. Comparison to other techniques of similar complexity, using identical classification procedures, shows the superiority of the proposed approach. Moreover, this approach leads directly to a real-time parallel implementation. >

[1]  Chrysostomos L. Nikias,et al.  Improved spectrum performance via a data-adaptive weighted burg technique , 1985, IEEE Trans. Acoust. Speech Signal Process..

[2]  T.F. Quatieri,et al.  Statistical model-based algorithms for image analysis , 1986, Proceedings of the IEEE.

[3]  Rama Chellappa,et al.  Texture classification using features derived from random field models , 1982, Pattern Recognit. Lett..

[4]  Michael Unser,et al.  Sum and Difference Histograms for Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[7]  Chrysostomos L. Nikias,et al.  Energy-weighted linear predictive spectral estimation: A new method combining robustness and high resolution , 1982 .

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

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

[10]  K. Laws.  Textured Image Segmentation , 1980 .

[11]  Chrysostomos L. Nikias,et al.  Frequency errors of the energy-weighted algorithm in processing sinusoidal signals: The analytic formula , 1983 .

[12]  Larry S. Davis,et al.  An Empirical Evaluation of Generalized Cooccurrence Matrices , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Chrysostomos Loizos Nikias.  A new class of robust spectral estimation algorithms based on minimum covariance recursion error and their application in cardiac data analysis , 1982 .

[14]  R. M. Haralick,et al.  Textural features for image classification. IEEE Transaction on Systems, Man, and Cybernetics , 1973 .

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