TY - GEN
T1 - Application of Fuzzy Logic in the Edge Detection of Real Pieces in Controlled Scenarios
AU - Vargas-Proa, José Daniel
AU - García-Martínez, Carlos Fabián
AU - Cano-Lara, Miroslava
AU - Rostro-González, Horacio
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Computer vision
KW - Edge detection
KW - Fuzzy inference
UR - http://www.scopus.com/inward/record.url?scp=85075675611&partnerID=8YFLogxK
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=pure_univeritat_ramon_llull&SrcAuth=WosAPI&KeyUT=WOS:000771909100029&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1007/978-3-030-33749-0_29
DO - 10.1007/978-3-030-33749-0_29
M3 - Conference contribution
AN - SCOPUS:85075675611
SN - 9783030337483
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 364
EP - 376
BT - Advances in Soft Computing - 18th Mexican International Conference on Artificial Intelligence, MICAI 2019, Proceedings
A2 - Martínez-Villaseñor, Lourdes
A2 - Batyrshin, Ildar
A2 - Marín-Hernández, Antonio
PB - Springer
T2 - 18th Mexican International Conference on Artificial Intelligence, MICAI 2019
Y2 - 27 October 2019 through 2 November 2019
ER -