Intermediate-level feature extraction in novel parallel environments
Parallel systems provide an approach to robust computing. The motivation for this work arises from using modem parallel environments in intermediate-level feature extraction. This study presents parallel algorithms for the Hough transform (HT) and the randomized Hough transform (RHT). The algorithms are analyzed in two parallel environments: multiprocessor computers and workstation networks. The results suggest that both environments are suitable for the parallelization of HT. Because scalability of the parallel RHT is weaker than with HT, only the multiprocessor environment is suitable. The limited scalability forces us to use adaptive techniques to obtain good results regardless of the number of processors. Despite the fact that the speedups with HT are greater than with RHT, in terms of total computation time, the new parallel RHT algorithm outperforms the parallel HT.
feature extraction randomized hough transform parallel hough transform multiprocessor systems clusters of workstations