TY - JOUR
T1 - Multi-view surface inspection using a rotating table
AU - Kaichi, Tomoya
AU - Mori, Shohei
AU - Saito, Hideo
AU - Sugano, Junichi
AU - Adachi, Hideyuki
N1 - Funding Information:
Shohei Mori received his B.S., M.S., and Ph.D. degrees in engineer- ing from Ritsumeikan University, Japan, in 2011, 2013, and 2016, respectively. He was in JSPS Research Fellowship for Young Scientists (DC-1) until 2016. He is currently in JSPS Research Fellowship for Young Scientists (PD) at Keio University and a guest researcher at Graz University of Technology.
Funding Information:
This research presentation is supported in part by a research assistantship of a Grant-in-Aid to the Program for Leading Graduate School for “Science for Development of Super Mature Society” from the Ministry of Education, Culture, Sport, Science, and Technology in Japan.
Publisher Copyright:
© 2018, Society for Imaging Science and Technology.
PY - 2018
Y1 - 2018
N2 - In this paper, we introduce a method to visually inspect in- dustrial parts by using multi-view images of an industrial part on a rotating table. During the visual inspection of the parts' sur- faces, the relationship between the camera pose, the light vectors, and the normal vectors of a part's surface is the key factor in de- tecting abnormalities. We can change the relationship between the three factors by rotating the part; then, the abnormalities are visible in certain positional relationships. We, therefore, track the points on a part's surfaces in an image sequence and discrimi- nate abnormal points (such as points on scratches or dents) from normal points based on pixel value transitions while rotating the part. The experimental results, which were based on real data of industrial parts, showed that the proposed method could detect the abnormalities on the surfaces.
AB - In this paper, we introduce a method to visually inspect in- dustrial parts by using multi-view images of an industrial part on a rotating table. During the visual inspection of the parts' sur- faces, the relationship between the camera pose, the light vectors, and the normal vectors of a part's surface is the key factor in de- tecting abnormalities. We can change the relationship between the three factors by rotating the part; then, the abnormalities are visible in certain positional relationships. We, therefore, track the points on a part's surfaces in an image sequence and discrimi- nate abnormal points (such as points on scratches or dents) from normal points based on pixel value transitions while rotating the part. The experimental results, which were based on real data of industrial parts, showed that the proposed method could detect the abnormalities on the surfaces.
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U2 - 10.2352/ISSN.2470-1173.2018.09.IRIACV-278
DO - 10.2352/ISSN.2470-1173.2018.09.IRIACV-278
M3 - Conference article
AN - SCOPUS:85052896085
SN - 2470-1173
SP - 2021
EP - 2026
JO - IS and T International Symposium on Electronic Imaging Science and Technology
JF - IS and T International Symposium on Electronic Imaging Science and Technology
T2 - Intelligent Robotics and Industrial Applications using Computer Vision 2018, IRIACV 2018
Y2 - 28 January 2018 through 1 February 2018
ER -