Exploiting task and data parallelism in parallel Hough and Radon transforms

Edge detection and shape detection in digital images are very computationally intensive problems. Parallel algorithms can potentially provide significant speedups while preserving the quality of the result obtained. Hough and Radon Transforms are projection-based transforms which are commonly used for edge detection and shape detection respectively. We propose in this paper various new parallel algorithms which exploit both task and data parallelism available in Hough and Radon transforms algorithms. A memory scalable aggressive task parallel algorithm is shown to be the most optimal algorithm in terms of memory scalability and performance on an IBM SP2.