Edge detection using fine-grained parallelism in VLSI

  • Authors:

  • Chetana Nagendra;Manjit Borah;Mohan Vishwanath;Robert M. Owens;Mary Jane Irwin

  • Affiliations:

  • Department of Computer Science, Pennsylvania State University, University Park, PA;Department of Computer Science, Pennsylvania State University, University Park, PA;Department of Computer Science, Pennsylvania State University, University Park, PA;Department of Computer Science, Pennsylvania State University, University Park, PA;Department of Computer Science, Pennsylvania State University, University Park, PA

  • Venue:
  • ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
  • Year:
  • 1993

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Abstract

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.