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 self-organizing 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.
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Ruiz-del-Solar, J., Kottow, D. (2000). Bio-inspired Texture Segmentation Architectures. In: Lee, SW., Bülthoff, H.H., Poggio, T. (eds) Biologically Motivated Computer Vision. BMCV 2000. Lecture Notes in Computer Science, vol 1811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45482-9_45
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DOI: https://doi.org/10.1007/3-540-45482-9_45
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