Edge Detection Method for Image Segmentation – A Survey of Soft Computing Approaches
Anubhuti Khare1, Manish Saxena2, Shweta Tiwari3 

1Anubhuti Khare, Reader, Department of Electronics and Communication, University Institute of Technology, Rajeev Gandhi Technical University, Bhopal, India.
2Manish Saxena, Head, Department of Electronics and Communication, Bansal Institute of Science and Technology Bhopal, India.
3Shweta Tiwari, Student, M.Tech (Digital Communication), Bansal Institute of Science and Technology Bhopal, India.
Manuscript received on August 19, 2011. | Revised Manuscript received on August 29, 2011. | Manuscript published on September 05, 2011. | PP: 174-178 | Volume-1 Issue-4, September 2011. | Retrieval Number: D0130081411/2011©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Soft Computing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. Soft computing techniques have found wide applications. One of the most important applications is edge detection for image segmentation. The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Edge is a boundary between two homogeneous regions. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. In this paper, the main aim is to survey the theory of edge detection for image segmentation using soft computing approach based on the Fuzzy logic, Genetic Algorithm and Neural Network.
Keywords: Image Segmentation, Edge Detection, Fuzzy logic, Genetic Algorithm, Neural Network.