A study of high-level recognition integrated system realizing scene understanding by using combination of coarsely- segmented image regions

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We have developed an image processing model and algorithms, where coarse regions are segmented and extracted irrespective of fine structures, and a scene image including plural objects is recognized and understood based on the relationship between segmented regions. We have developed VLSIs implementing these algorithms and an integrated system for real-time higher-order image recognition. In the developed system, coarse region segmentation by a resistive-fuse network and elastic graph matching (EGM) are performed in FPGAs connected with a PC via USB. Related fundamental technologies have also been developed.In coarsely segmented regions, when an object is partly hidden by another object and separated into two regions, we have succeeded to extract two separated regions simultaneously by considering the brightness of the regions when synchronous firing in Terman-Wang nonlinear oscillator networks is used. Furthermore, we have proposed a cellular-automaton-type region extraction algorithm based on a simplified oscillator network model, and a pixel-parallel digital circuit performing this algorithm, which has been implemented in an FPGA.In order to compensate for lacking of edge information in coarse region segmentation, we have proposed two digital and analog-type pixel-parallel anisotropic diffusion algorithms and circuits for generating subjective contours.These proposed algorithms have been implemented in a CMOS digital circuit and merged A/D circuit, respectively. Using these diffusion algorithms, we have successfully generated subjective contours for typical figures such as a Kanizsa's triangle.