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Adaptive hyperplane algorithm for texture characterization

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2 Author(s)
Hajeer, E.K. ; Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA ; Sethi, I.K.

Textural features are often consider as one of the most powerful features in describing the intrinsic physical properties of object surfaces in a scene. In this paper, we propose characterizing image textures by a least-squares hyperplane fitting of their image multidimensional primitives. The adaptive implementation of the hyperplane fitting process is carried out by a newly proposed nonlinear supervised neural unit trained by a constrained form of anti-Hebbian learning. Experimental results are presented to demonstrate the performance of the proposed texture characterization model in image classification and segmentation applications

Published in:

Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on  (Volume:3 )

Date of Conference:

2-5 Oct 1994

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