Rule-based soft computing for edge detection

Rule-based soft computing for edge detection In this paper, we present a robust rule-based edge detection method. Although generalized edge detection approaches are effective for most images they often fail in others. Thus the goal of our method is to provide more reliable edge detection results that are effective in most images. We implement the proposed method as follows: (1) transform RGB images to YCbCr format, (2) apply Sobel mask in four edge directions (horizontal, vertical, diagonal, anti-diagonal), (3) apply a bi-directional mask in four edge directions (horizontal–diagonal, vertical–diagonal, horizontal–anti-diagonal, vertical–anti-diagonal), and (4) detect rule-based edges by calculating membership degrees. Simulation results demonstrate that the proposed method is effective in most given images. We used three benchmarks approaches (Canny edge mask, high-pass filter, and Sobel mask) to compare the subjective performance quality. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Rule-based soft computing for edge detection

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-016-4329-7
Publisher site
See Article on Publisher Site

Abstract

In this paper, we present a robust rule-based edge detection method. Although generalized edge detection approaches are effective for most images they often fail in others. Thus the goal of our method is to provide more reliable edge detection results that are effective in most images. We implement the proposed method as follows: (1) transform RGB images to YCbCr format, (2) apply Sobel mask in four edge directions (horizontal, vertical, diagonal, anti-diagonal), (3) apply a bi-directional mask in four edge directions (horizontal–diagonal, vertical–diagonal, horizontal–anti-diagonal, vertical–anti-diagonal), and (4) detect rule-based edges by calculating membership degrees. Simulation results demonstrate that the proposed method is effective in most given images. We used three benchmarks approaches (Canny edge mask, high-pass filter, and Sobel mask) to compare the subjective performance quality.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jan 23, 2017

References

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