Reviewing Soft Computing Approaches for Edge Detection: Hybrid and Non-hybrid

Abstract

Soft Computing is a multifaceted technique comprising of Fuzzy Logic, Neural Network, Genetic algorithms and other Evolutionary computation. These paradigms have found wide variety of applications in the field of image processing. One of the most vital applications of image segmentation is edge detection where edge refers to the boundary between two consistent regions and edge detection is the process of detecting and finding abrupt discontinuities in an image. This paper summarizes hybrid and non-hybrid approaches for edge detection. The objective of this paper is to survey the core issues for soft computing based approaches for edge detection.

Performance Analysis of Soft Computing and Image Processing Based Image Segmentation

“…Watershed based image segmentation [4], sometimes also referred in general term as watershed technique is a strong mathematical morphological tool for the image segmentation. Watershed based segmentation is popular in the areas like biomedical & medical imaging processing, and computer vision [5].…”