A parallel approach to hybrid range image segmentation

Parallel processing methods are an attractive means to achieve significant speedup of computationally expensive image understanding algorithms, such as those applied to range images. Mixed-mode parallel systems are ideally suited to this area because of the flexibility in using the different modes of parallelism. The trade-offs of using different parallel modes are examined through the implementation of hybrid range segmentation operations, characteristic of a broad class of low level image processing algorithms. Alternative means of distributing data among the processing elements that achieve improved performance are considered. Results comparing different implementations on a single reconfigurable parallel processor. PASM, indicate some generally applicable guidelines for the effective parallelization of vision algorithms.<>