A hybrid edge detection method based on fuzzy set theory and cellular learning automata
In this paper, a hybrid edge detection method based on fuzzy sets and cellular learning automata is proposed. At first, existing methods of edge detection and their problems are discussed and then a high performance method for edge detection, that can extract edges more precisely by using only fuzzy...
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| Main Authors: | , , , |
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| Format: | Book Section |
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IEEE Computer Society
2009
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/11791/ http://dx.doi.org/10.1109/ICCSA.2009.19 |
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| Summary: | In this paper, a hybrid edge detection method based on fuzzy sets and cellular learning automata is proposed. At first, existing methods of edge detection and their problems are discussed and then a high performance method for edge detection, that can extract edges more precisely by using only fuzzy sets than by other edge detection methods, is suggested. After that the edges improve incredibly by using cellular learning automata. In the end, we compare it with popular edge detection methods such as Sobel and Canny. The proposed method does not need parameter settings as Canny edge detector does, and it can detect edges more smoothly in a shorter amount of time while other edge detectors cannot. |
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