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Proceedings Paper

Connectionist learning procedure for edge detector
Author(s): Chen-Huei Chang; Chao-Chih Chang; Shu-Yuen Hwang
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Paper Abstract

Mask matching is one method for edge detection which convolves patterns in various orientations with the given image. The orientation that gives the best match at a given point is decided as the edge orientation at that point, and the magnitude of this best match as a measure of the edge strength. However, detectors are usually designed heuristically or are designed based on some assumed distribution of pixels. Thus, how to select an appropriate edge detector for a specific type of images is by no means an easy job. This paper presents a connectionist procedure for learning the appropriate edge detector for a specific class of images. The delta learning rule is used to train a neural network and the effort of designing edge detectors is done automatically. The experimental results show that this learning approach is promising.

Paper Details

Date Published: 1 February 1992
PDF: 12 pages
Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57061
Show Author Affiliations
Chen-Huei Chang, National Chiao Tung Univ. (Taiwan)
Chao-Chih Chang, National Chiao Tung Univ. (Taiwan)
Shu-Yuen Hwang, National Chiao Tung Univ. (Taiwan)


Published in SPIE Proceedings Vol. 1607:
Intelligent Robots and Computer Vision X: Algorithms and Techniques
David P. Casasent, Editor(s)

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