Assessment of the performance of computer vision algorithms and parallel architectures

The performance of the existing algorithms is generally assessed without any considerations to the architecture or technology that would be used for implementation. In many instances, the performance of the algorithms is degraded when they are implemented on a specific architecture. To date no formal assessment method exists that considers the effect of implementation constraints on the performance of the algorithms. In this investigation, the authors present a novel structured approach that can be used in assessing suitability of implementing algorithms on a specific architecture and in the comparison of performance of different architectures. The performance is measured in terms of a figure of merit that combines both accuracy of results and implementation efficiency. Some special considerations are mentioned that can further enhance the performance.