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Proceedings Paper

Image pattern algorithms using neural networks
Author(s): Takis Kasparis; George Eichmann; Michael Georgiopoulos; Gregory L. Heileman
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Paper Abstract

The ability to classify texture regions in images is considered to be an important aspect of scene analysis. The information gained from such classification can be used by a computer vision system to assist in image segmentation as well as object identification. In this paper, the use of a neural network model in performing classification of images containing regular textures is investigated. The texture features used in the classification process are Hough transform-based descriptors. The performance and capabilities of the neural network approach are then compared to classical technique utilizing a linear associative memory.

Paper Details

Date Published: 1 September 1990
PDF: 9 pages
Proc. SPIE 1297, Hybrid Image and Signal Processing II, (1 September 1990); doi: 10.1117/12.21323
Show Author Affiliations
Takis Kasparis, Univ. of Central Florida (United States)
George Eichmann, City College/CUNY (United States)
Michael Georgiopoulos, Univ. of Central Florida (United States)
Gregory L. Heileman, Univ. of Central Florida (United States)


Published in SPIE Proceedings Vol. 1297:
Hybrid Image and Signal Processing II
David P. Casasent; Andrew G. Tescher, Editor(s)

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