Parallel image matching on a distributed system

Image matching based on image feature pixels involves heavily iterated computation and repeated memory access. In our previous work the detection of interesting points has been reported as an efficient pre-processing step to extract binary images for further matching in terms of certain distance measurement. This paper presents our extension to a parallel implementation of the matching scheme for object recognition on a low cost heterogeneous PVM (Parallel Virtual Machine) network. While most of the sequential execution time is spent on image feature extraction, distance transform and matching measurement, our investigation shows that a distributed memory multicomputer can best meet the high computational and memory access demands in image processing. The performance is evaluated in terms of execution time. We conclude that parallel image processing con be implemented on a general distributed system to achieve the speedup without specific hardware requirement