Image characterizations based on joint gray level-run length distributions
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Evolving descriptors for texture segmentation
Pattern Recognition
Computers in Biology and Medicine
Collaboration of reconfigurable processors in grid computing: Theory and application
Future Generation Computer Systems
Classification of dermatological ulcers based on tissue composition and color texture features
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Sorted random projections for robust rotation-invariant texture classification
Pattern Recognition
Collaboration of reconfigurable processors in grid computing for multimedia kernels
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
Texture classification using features derived from random field models
Pattern Recognition Letters
SVM and haralick features for classification of high resolution satellite images from urban areas
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Fast texel size estimation in visual texture using homogeneity cues
Pattern Recognition Letters
International Journal of Data Mining and Bioinformatics
Performance divergence with data discrepancy: a review
Artificial Intelligence Review
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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.