A new edge detection method based on global evaluation using fuzzy clustering


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Flores-Vidal, Pablo A. and Olaso, Pablo and Gómez, Daniel and Guada, Carely (2019) A new edge detection method based on global evaluation using fuzzy clustering. Soft Computing, 23 (6). pp. 1809-1821. ISSN 1432-7643

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Official URL: https://doi.org/10.1007/s00500-018-3540-z


Traditionally, the edge detection process requires one final step that is known as scaling. This is done to decide, pixel by pixel, if these will be selected as final edge or not. This can be considered as a local evaluation method that presents practical problems, since the edge candidate pixels should not be considered as independent. In this article, we propose a strategy to solve these problems through connecting pixels that form arcs, that we have called segments. To accomplish this, our edge detection algorithm is based on a more global evaluation inspired by actual human vision. Our paper further develops ideas first proposed in Venkatesh and Rosin (Graph Models Image Process 57(2):146–160, 1995). These segments contain visual features similar to those used by humans, which lead to better comparative results against humans. In order to select the relevant segments to be retained, we use fuzzy clustering techniques. Finally, this paper shows that this fuzzy clustering of segments presents a higher performance compared to other standard edge detection algorithms.

Item Type:Article
Uncontrolled Keywords:Edge detection, Global evaluation, Supervised classification, Fuzzy clustering, Edge segments
Subjects:Sciences > Mathematics > Cybernetics
Sciences > Mathematics > Set theory
ID Code:54959
Deposited On:08 Apr 2019 16:30
Last Modified:09 Apr 2019 08:46

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