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Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/57070
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dc.contributor.authorChukit Ruanpengen_US
dc.contributor.authorSansanee Auephanwiriyakulen_US
dc.contributor.authorNipon Theera-Umponen_US
dc.date.accessioned2018-09-05T03:34:33Z-
dc.date.available2018-09-05T03:34:33Z-
dc.date.issued2017-08-23en_US
dc.identifier.issn10987584en_US
dc.identifier.other2-s2.0-85030152769en_US
dc.identifier.other10.1109/FUZZ-IEEE.2017.8015771en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85030152769&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/57070-
dc.description.abstract© 2017 IEEE. In this paper, the simple movement (walking dog, crawling human, and walking human) recognition system using the Mamdani fuzzy inference system is introduced. The membership functions of each input feature are generated automatically without experts' prior knowledges. The system produces a very high recognition rate, i.e., 93.97%, on the validation set of the cross validation. However, there are some misclassifications between walking dog and crawling human classes. The misclassifications are mainly from the incomplete segmentation of the objects of interest.en_US
dc.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleHuman and dog movement recognition using fuzzy inference system with automatically generated membership functionsen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleIEEE International Conference on Fuzzy Systemsen_US
article.stream.affiliationsChiang Mai Universityen_US
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