Abstract
Edges detection in image processing is more facilitate in the process of feature extraction, segmentation and classification, for these a proper detection of edges to be required. In conventional edge detectors, edges are not properly defined because of improper selection of threshold. In this research, comparative analyses of three algorithms for same goal that is the detection of edges in an image processing application have been shown. Here, Sobel edge detector operator is used with fuzzy logic techniques such as type-1 and interval type-2 fuzzy logic in the process of edge detection in the images. Since, fuzzy logic deals with uncertainty and vagueness in the problems by the use of linguistic variables and rule base applied to the fuzzy logic system. Finally, results show that interval type-2 fuzzy edge detectors provided better results among all mentioned techniques on the basis of total edge-detected pixel in an output image. Because in IT2FLS, whereas membership function itself in Type-1 Fuzzy which gives more precise results.
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The database of input images for edge detection has been created from Google search. The links of respective input images are mentioned in references.
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Bhogal, R.K., Agrawal, A. (2019). Image Edge Detection Techniques Using Sobel, T1FLS, and IT2FLS. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 107. Springer, Singapore. https://doi.org/10.1007/978-981-13-1747-7_29
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DOI: https://doi.org/10.1007/978-981-13-1747-7_29
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