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Proceedings Paper

Adaptive classification of textured images using moments and autoregressive models
Author(s): Levon Sukissian; Andreas Tirakis; Stefanos D. Kollias
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Paper Abstract

An adaptive approach to the classification of textured images is presented, based on the extraction of appropriate features from images. Autoregressive linear prediction models, as well as moments of images, are features which are examined and compared in the paper. Classification is achieved in an adaptive way, using an artificial feedforward neural network, which is trained by examples, using an efficient variant of the backpropagation learning algorithm. It is also shown that an adaptive least squares estimation algorithm can be appropriately interweaved with the network, resulting in an on-line adaptive classification scheme. Simulation results are given, which illustrate the performance of the presented method.

Paper Details

Date Published: 1 September 1990
PDF: 11 pages
Proc. SPIE 1360, Visual Communications and Image Processing '90: Fifth in a Series, (1 September 1990); doi: 10.1117/12.24145
Show Author Affiliations
Levon Sukissian, National Technical Univ. of Athens (Greece)
Andreas Tirakis, National Technical Univ. of Athens (Greece)
Stefanos D. Kollias, National Technical Univ. of Athens (Greece)


Published in SPIE Proceedings Vol. 1360:
Visual Communications and Image Processing '90: Fifth in a Series
Murat Kunt, Editor(s)

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