Scalable parallel implementations of perceptual grouping on connection machine CM-5

Perceptual grouping is a key step in vision to organize image data into structural hypotheses to be used for high level analysis. We propose data allocation and load balancing strategies which reduce the communication cost and evenly distribute the grouping operations among the processors. These techniques result in scalable algorithms for performing perceptual grouping on CM-5. The performance of our algorithms depends only on the total grouping operations generated by the image data and is independent of the distribution of the data among the processors. Our implementations show that given a 1 K×1 K input image, extraction of line segments and several perceptual grouping steps can be performed in 5.0 seconds using a partition of CM-5 having 32 processing nodes. A serial implementation of these steps on a Sun Sparc 400 takes more than 2 minutes