Abstract
Industrial processes such as manufacturing and machining parts, fault detection and quality control are some of the areas of study that encompass computational vision techniques, image processing and currently fuzzy logic. Particularly, the edge detection of objects in captured images is a technique widely used in industrial automated systems. In this work, we propose a technique for edge detection in digital images obtained from real pieces based on fuzzy logic. The fuzzy inference model works with 18 Mamdani type rules and was built with 8 input variables and one output variable. It is, the processing of the image was performed under the conditions of the lighting scenario, background and the color of the piece. The performance of the algorithm was evaluated on several images captured from different work environments and it was compared with traditional computer vision methods using gradient operators. The use of fuzzy logic in image processing expands the possibilities to solve a problem and provides more answers over the restrictions of classical methods.
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CONACYT Project FC2016-1961 “Neurociencia computacional: de la teoría del desarrollo de sistemas neuromórficos”.
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Vargas-Proa, J.D., García-Martínez, C.F., Cano-Lara, M., Rostro-González, H. (2019). Application of Fuzzy Logic in the Edge Detection of Real Pieces in Controlled Scenarios. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_29
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