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Committee Date Time Place Paper Title / Authors Abstract Paper #
PRMU, IBISML, IPSJ-CVIM [detail] 2023-03-02
10:35		 Hokkaido Future University Hakodate
(Primary: On-site, Secondary: Online)		 Investigation of Appearance Inspection Method Considering the Number of Corresponding Local Patches
Katsuhisa Kitaguchi, Yohei Nishizaki, Mamoru Saito (ORIST) PRMU2022-74 IBISML2022-81		 There has been a great deal of research on appearance inspection using deep learning, which learns only from normal imag... [more] PRMU2022-74 IBISML2022-81
pp.88-92
PRMU 2020-12-17
15:25		 Online Online Visual inspection system with a small number of anomalous data using DevNet
Katsuhisa Kitaguchi, Yohei Nishizaki, Mamoru Saito (ORIST) PRMU2020-47		 A good visual inspection using deep learning needs to collect a large amount of anomalous data. To solve this problem, w... [more] PRMU2020-47
pp.53-57
PRMU, IBISML, IPSJ-CVIM [detail] 2018-09-21
10:20		 Fukuoka   Analysis of the Resion of Interest in Visual Inspection using CNN
Katsuhisa Kitaguchi, Yohei Nishizaki, Mamoru Saito (ORIST) PRMU2018-59 IBISML2018-36		 We have been developing a deep learning-based method for automated visual inspection of deep drawing metal products. Dee... [more] PRMU2018-59 IBISML2018-36
pp.143-147
PRMU 2017-10-13
15:20		 Kumamoto   Automatic Visual Inspection of Metal Pressed Parts Using CNN
Katsuhisa Kitaguchi, Yohei Nishizaki, Mamoru Saito (ORIST) PRMU2017-95		 We have been developing a deep learning-based method for automated visual inspection of deep drawing metal products. Dee... [more] PRMU2017-95
pp.181-186
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