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A Theoretical Comparison of Texture Algorithms
March 1980 (vol. 2 no. 3)
pp. 204-222
Richard W. Conners, MEMBER, IEEE, Department of Electrical Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803.
Charles A. Harlow, MEMBER, IEEE, Department of Electrical Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803.
An evaluation of the ability of four texture analysis algorithms to perform automatic texture discrimination will be described. The algorithms which will be examined are the spatial gray level dependence method (SGLDM), the gray level run length method (GLRLM), the gray level difference method (GLDM), and the power spectral method (PSM). The evaluation procedure employed does not depend on the set of features used with each algorithm or the pattern recognition scheme. Rather, what is examined is the amount of texturecontext information contained in the spatial gray level dependence matrices, the gray level run length matrices, the gray level difference density functions, and the power spectrum. The comparison will be performed in two steps. First, only Markov generated textures will be considered. The Markov textures employed are similar to the ones used by perceptual psychologist B. Julesz in his investigations of human texture perception. These Markov textures provide a convenient mechanism for generating certain example texture pairs which are important in the analysis process. In the second part of the analysis the results obtained by considering only Markov textures will be extended to all textures which can be represented by translation stationary random fields of order two. This generalization clearly includes a much broader class of textures than Markovian ones. The results obtained indicate that the SGLDM is the most powerful algorithm of the four considered, and that the GLDM is more powerful than the PSM.
Citation:
Richard W. Conners, Charles A. Harlow, "A Theoretical Comparison of Texture Algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 2, no. 3, pp. 204-222, March 1980, doi:10.1109/TPAMI.1980.4767008
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