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Texture plays an important role in vision research and in many practical applications, as well. Texture reseach has been wide in applied sciences, and thus many methods for texture analysis have been proposed. To recognize texture in real time modern optics may offer new tools, which is the theme of the present paper. We investigate the applicability of complex matched filter, and phaseonly filters to recognize textures. Natural textures of the Brodatz collection3 were used as the test samples. We show that optical correlators are useful in texture recognition. The recognition is based on the peak-to-correlation ratio, which is much higher for phase-only filters than for the matched filter. However, if we consider output signal-to-noise ratio the matched filter remains valuable.

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An artificial vision system with spatio-chromatic channels is proposed. A dynamic neural network is used for the spatial and chromatic information of a scene. The spatio-chromatic information is transmitted into two channels for processing. This segregation allows accurate spatial and chromatic analysis of the visual input. For both channels, models based on the biology of the visual system are used. Spatial channel responses simulate e.g. enhanced edges and subjective contours. Chromatic channel output is shown to correspond to the color characteristics found in the spectral color tests and in the literature of the physiology of color vision. The ultimate gool of the project is to find biologically motivated model for an intelligent image sensor. In this report we describe potential candidates for both, spatial and chromatic information.