Bio-Inspired Texture Segmentation Architectures

Authors:
Javier Ruiz-del-Solar;Daniel Kottow
Affiliations:
-;-
Venue:
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Year:
2000

Citing 2
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Abstract

This article describes three bio-inspired Texture Segmentation Architectures that are based on the use of Joint Spatial/Frequency analysis methods. In all these architectures the bank of oriented filters is automatically generated using adaptive-subspace self-organizing maps. The automatic generation of the filters overcomes some drawbacks of similar architectures, such as the large size of the filter bank and the necessity of a priori knowledge to determine the filters' parameters. Taking as starting point the ASSOM (Adaptive-Subspace SOM) proposed by Kohonen, three growing selforganizing networks based on adaptive-subspace are proposed. The advantage of this new kind of adaptive-subspace networks with respect to ASSOM is that they overcome problems like the a priori information necessary to choose a suitable network size (the number of filters) and topology in advance.