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A Dynamic Network Model of the Color Visual Pathways for Attentive Recognition

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Abstract

A neural network architecture for the segmentation and recognition of colored and textured visual stimuli is presented. The architecture is based on the Boundary Contour System and Feature Contour System (BCS/FCS) of S. Grossberg and E. Mingolla. The architecture proposes a biologically-inspired mechanism for color processing based on antagonist interactions. It suggests how information from different modalities (i.e. color or texture) can be fused together to form a coherent segmentation of the visual scene. It identifies two stages of visual pattern recognition, namely, a global preattentive recognition of the visual scene followed by a local attentive recognition within a particular visual context. The global and local classification and recognition of visual stimuli use ART-type models of G. Carpenter and S. Grossberg for pattern learning and recognition based on color and texture. One example is presented corresponding to an figure-figure separation task. The architecture provides a mechanism for segmentation, categorization and recognition of images from different classes based on self-organizing principles of perception and pattern recognition.

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Authors and Affiliations

  1. Department of Signal Theory, Communications and Telematics Engineering School of Telecommunications Engineering, University of Valladolid, Real de Burgos S/N, 47011, Valladolid, Spain. E-mail

    Francisco Díaz-Pernas

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  1. Francisco Díaz-Pernas
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Díaz-Pernas, F. A Dynamic Network Model of the Color Visual Pathways for Attentive Recognition. Neural Processing Letters 7, 27–36 (1998). https://doi.org/10.1023/A:1009628520777

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