Image segmentation through Gabor-based neural networks

J. Mario Aguilar; Jose L. Contreras-Vidal

doi:10.1117/12.140037

16 September 1992 Image segmentation through Gabor-based neural networks

J. Mario Aguilar, Jose L. Contreras-Vidal

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.140037
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

J. Mario Aguilar and Jose L. Contreras-Vidal "Image segmentation through Gabor-based neural networks", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140037

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