Edge detection using fine-grained parallelism in VLSI

This paper demonstrates 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 being developed at the Pennsylvania State University, is shown which computes at the rate of approximately 75,000 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 general case of Radon transforms.