A new edge detection approach based on fuzzy segments clustering

Abstract

Edge detection comprises different stages that go from adaptation of the original image -conditioning- to the selection of the definitive edges. This last step, known as scaling, requires the application of a thresholding process over the gradients of luminosity values of the pixels. Traditionally, this is made through a local evaluation process that works pixel by pixel. As the edge candidate pixels are not independent, a wider strategy suggests the use of a more global evaluation. In this sense, this approach resembles more the human vision. This paper further develops ideas related to edge lists, first proposed in 1995 [1]. This paper will refer to edge lists as edge segments. These segments contain visual features similar to the ones that humans use, which might lead to better comparative results. In this paper we propose using clustering techniques to differentiate the appropriate segments or true segments from the false ones, and we introduce an algorithm that uses fuzzy clustering techniques. Finally, this paper shows that this fuzzy clustering over the segments performs at least as well as other standard algorithms used for edge detection.