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Generalized Approach to Split and Merge Segmentation on Parallel Architectures

Parallel computers are playing an increasingly important role in areas such as fluid dynamics, particle physics, simulation, and computer vision. In particular, regular data structures and the complexity of computation of most computer vision algorithms make them ideally suited for implementation in parallel computers. In the past, most parallel algorithms have been developed for a given target architecture. Recently, generalized approaches to parallel program design, where architectural issues are postponed to the last step of the design process, have been gaining momentum in the research community. A formal approach for the design of parallel programs is to iteratively refine the specifications. In this paper, we demonstrate the use of this approach to split and merge method of segmentation. The starting point for the design is the general specifications of the divide-and-conquer paradigm, and the end result is the design of a program for the split and merge segmentation on a hypercube architecture. The performance is evaluated in terms of speed-up and efficiency of the processors.