Edge image aggregation method using ordered weighted averaging functions

The proposal of new edge detectors is a constant in last years due to the importance of the edge detection result for high-level image processing tasks. It is known that there is no optimal edge detector for all kinds of images and in addition, it is not a straightforward mission to set the optimal set of parameters of the edge detector for each image. In this paper, an algorithm to deal with these drawbacks is presented based on obtaining a consensus edge image from the edge images obtained by several edge detectors or configurations of the same edge detector. These input edge images are combined using multifuzzy sets and averaging aggregation functions and after applying a penalty function, the consensus edge image is obtained. Experimental results show that this approach leads to a robust edge detector which is able to obtain notable results for different kinds of images.