Parallel Algorithms for Perceptual Grouping on Distributed Memory Machines

Perceptual grouping is a key intermediate-level vision problem. Parallel solutions to this problem are characterized by uneven distribution of symbolic features among the processors, unbalanced workload, and irregular interprocessor data dependency caused by the input image. In this paper, we propose two load-balancing techniques for parallelizing perceptual grouping on distributed-memory machines. By using an initial workload estimate, we first partition the computations to distribute the workload across the processors. In addition, we asynchronously perform ongoing task migrations to adapt to the unbalanced workload which may evolve differently from the initial estimate. We also discuss two strategies to manage the irregular interprocessor data dependency. To illustrate our ideas, perceptual grouping steps used in an integrated vision system for building detection are used as examples. Our experimental results show that, given 8K extracted line segments from a 1K × 1K image, both the line and junction grouping steps can be completed in 0.644 s on a 32-node SP2 and in 0.585 s on a 32-node T3D. For the same grouping steps, a serial implementation requires 10.550 s and 10.023 s on a single node of SP2 and T3D, respectively. The implementations were performed using the message passing interface standard and are portable to other high performance computing platforms. © 1998 Academic Press.link_to_subscribed_fulltex