A connectionist algorithm for image segmentation

An edge-based segmentation algorithm based on the knowledge in human vision was developed. The research followed Grossberg's boundary contour system and developed a parallel distributive algorithm which consists of multiple processing stages--mainly anisotropic edge filtering, corner detection, and spatial coherence check. The two-dimensional input information is processed in parallel within each stage and pipelined among stages. Within each stage, local operations are performed at each pixel. The application of this algorithm to many test patterns shows that the algorithm gives good segmentation and behaves reasonably well against random noise. A multiscale mechanism in the algorithm can segment an object into contours at different levels of detail. The algorithm was compared with an approximation of Grossberg's boundary contour system. Both algorithms gave reasonable performance for segmentation. The differences lie in the level of image dependency of the configuration parameters of the algorithm. Also, the way random noise affects the algorithm was compared with the way it affects human object detection. Data obtained from psychophysical experiments and from application of the algorithm show a similar trend.

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