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Nonlinear Operators for Improving Texture Segmentation Based on Features Extracted by Spatial Filtering

M. Unser, M. Eden

IEEE Transactions on Systems, Man, and Cybernetics, vol. 20, no. 4, pp. 804-815, July-August 1990.



An unsupervised texture segmentation system using texture features obtained from a combination of spatial filters and nonlinear operators is described. Local texture features are evaluated in parallel by a succession of four basic operations: (1) a convolution for local structure detection (local linear transform); (2) a first nonlinearity of the form f(x) = |x|α; (3) an iterative smoothing operator; and (4) a second nonlinearity g(x). The Karhunen-Loève transform is used to reduce the dimensionality of the resulting feature vector, and segmentation is achieved by thresholding or clustering in feature space. The combination of nonlinearities f(x) = |x|α (in particular, α = 2) and g(x) = log x maximizes texture discrimination, and results in a description with variances approximately constant for all feature components and texture regions. This latter property improves the performance of both feature reduction and clustering algorithms significantly.


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