Opposition-Based Window Memoization for Morphological Algorithms

In this paper we combine window memoization, a performance optimization technique for image processing, with opposition-based learning, a new learning scheme where the opposite of data under study is also considered in solving a problem. Window memoization combines memoization techniques from software and hardware with the repetitive nature of image data to reduce the number of calculations required for an image processing algorithm. We applied window memoization and opposition-based learning to a morphological edge detector and found that a large portion of the calculations performed on pixels neighborhoods can be skipped and instead, previously calculated results can be reused. The typical speedup for window memoization was 1.42. Combining window memoization with opposition-based learning yielded a typical increase of 5% in speedups