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Nonlinear operators for improving texture segmentation based on features extracted by spatial filtering

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2 Author(s)
Unser, Michael ; Nat. Inst. of Health, Bethesda, MD, USA ; Eden, M.

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-Loeve 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

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:20 ,  Issue: 4 )

Date of Publication:

Jul/Aug 1990

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