Edge detection using fine-grained parallelism in VLSI

The authors demonstrate an optimal time algorithm and architecture for edge detection in real time using fine grained parallelism. Given an image in the form of a two-dimensional array of pixels, this algorithm computes the Sobel and Laplacian operators for skimming lines in the image and then generates the Hough array using thresholding Hough transforms for M different angles of projection are obtained in a fully systolic manner without using any multiplication or division. An implementation of the algorithm on the MGAP-a fine-grained processor array architecture developed at the Pennsylvanian State University-is shown. It computes at the rate of approximately 75000 Hough transforms per second on a 256*256 image using a 25-MHz clock. It is also shown that the algorithm can be easily extended to the general case of Radon transforms.<>