Connectionist learning procedure for edge detector

Chen-Huei Chang; Chao-Chih Chang; Shu-Yuen Hwang

doi:10.1117/12.57061

1 February 1992 Connectionist learning procedure for edge detector

Chen-Huei Chang, Chao-Chih Chang, Shu-Yuen Hwang

Proceedings Volume 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques; (1992) https://doi.org/10.1117/12.57061
Event: Robotics '91, 1991, Boston, MA, United States

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.

Citation Download Citation

Chen-Huei Chang, Chao-Chih Chang, and Shu-Yuen Hwang "Connectionist learning procedure for edge detector", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); https://doi.org/10.1117/12.57061

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