RT Journal Article
T1 A new edge detection method based on global evaluation using fuzzy clustering
A1 Flores Vidal, Pablo Arcadio
A1 Olaso, Pablo
A1 Gómez González, Daniel
A1 Guada, Carely
AB 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.
SN 1432-7643
YR 2019
FD 2019-03
LK https://hdl.handle.net/20.500.14352/13224
UL https://hdl.handle.net/20.500.14352/13224
LA eng
NO Flores-Vidal PA, Olaso P, Gómez D, Guada C (2019) A new edge detection method based on global evaluation using fuzzy clustering. Soft Comput 23:1809–1821. https://doi.org/10.1007/s00500-018-3540-z
NO Ministerio de Ciencia, Innovación y Universidades (España)
DS Docta Complutense
RD 27 dic 2025