Reviewing Soft Computing Approaches for Edge Detection: Hybrid and Non-hybrid
Manisha Kaushal and Akashdeep
UIET, Panjab University Chandigarh
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.
Index Terms—soft computing, neural networks, genetic algorithm, fuzzy logic, hybrid
Cite: Manisha Kaushal and Akashdeep, "Reviewing Soft Computing Approaches for Edge Detection: Hybrid and Non-hybrid," Journal of Emerging Technologies in Web Intelligence, Vol. 5, No. 4, pp. 372-379, November 2013. doi:10.4304/jetwi.5.4.372-379
Index Terms—soft computing, neural networks, genetic algorithm, fuzzy logic, hybrid
Cite: Manisha Kaushal and Akashdeep, "Reviewing Soft Computing Approaches for Edge Detection: Hybrid and Non-hybrid," Journal of Emerging Technologies in Web Intelligence, Vol. 5, No. 4, pp. 372-379, November 2013. doi:10.4304/jetwi.5.4.372-379
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