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Proceedings Paper

Image segmentation through Gabor-based neural networks
Author(s): J. Mario Aguilar; Jose L. Contreras-Vidal
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Paper Abstract

An image segmentation system based on known cortical interactions and topography is presented. First, a space variant retinal-like representation of the image is obtained not only to provide with spatial focusing but also to reduce the computational load. Next, a layer of receptive fields for feature extraction was evolved from a set of Gabor functions with multiple orientational characteristics. Shunting competitive interactions among different features eliminate local ambiguities. Long-range interactions among `winning' cells, sharing similar orientation preferences, but with possible different spatial scales, are used to form a congruent description of the visual scene taking into account spatial context. The output of this layer is used to partition the image into emergent segments. Encouraging results of MRI images processing are presented.

Paper Details

Date Published: 16 September 1992
PDF: 8 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140037
Show Author Affiliations
J. Mario Aguilar, Boston Univ. (United States)
Jose L. Contreras-Vidal, Boston Univ. (United States)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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