Edge detection using fine-grained parallelism in VLSI

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationPlenary, Special, Audio, Underwater Acoustics, VLSI, Neural Networks
PublisherPubl by IEEE
ISBN (Print)0780309464
StatePublished - 1993
Event1993 IEEE International Conference on Acoustics, Speech and Signal Processing - Minneapolis, MN, USA
Duration: Apr 27 1993Apr 30 1993


Other1993 IEEE International Conference on Acoustics, Speech and Signal Processing
CityMinneapolis, MN, USA

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics


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