{"type":"ScholarlyArticle","title":"Neural Network Based Photometric Strereo Using Illumination Planning","authors":[{"name":"Yuji Iwahori","slug":"yuji-iwahori","given":"Yuji","family":"Iwahori","affiliation":""},{"name":"Wataru Kato","slug":"wataru-kato","given":"Wataru","family":"Kato","affiliation":""},{"name":"Shoaib Bhuiyab","slug":"shoaib-bhuiyab","given":"Shoaib","family":"Bhuiyab","affiliation":""},{"name":"Robert J. Woodham","slug":"robert-j-woodham","given":"Robert J.","family":"Woodham","affiliation":""},{"name":"Naohiro Ishii","slug":"naohiro-ishii","given":"Naohiro","family":"Ishii","affiliation":""}],"abstract":"This paper proposes a new approach of neural network based photom etric stereo in which all the directions of illumination are close to and is rotationally moved around the viewing direction . Adjace nt light sources cause a num erically ill-conditioned problem. Ill-conditioning is overcome in two ways. First , many more than thr ee images are acquired. Second , principal components analysis is used as a linear preprocessing technique to determine a r educed dimensionality subspace for use as input. Th e approach is empiri cal and us es the radial basis function (RBF) neural network to do non-parametric functional approximation. One neural network maps image irradiance to surface normal. A second network maps surface normal to image irradiance. Th e two networks are trained using samples from a ca libr ation sphere. Comparison between th e actual input and the inversely predicted input is used as a confiden ce est imat e. Further , we introduc e th e illumination planning , which dispos es the information of th e cast shadow region from th e ill-effected lig ht sources for each point on th e object using the confidence estimate. Th en , the best combination of NN units with well-effected light sources for each point are used to get the relevant results . The results on real data are also eva luat ed.","doi":null,"url":"https://ijcai.org/proceedings/1997/215","pdf":"https://ijcai.org/proceedings/1997/0215.pdf","markdown":"https://ijcai.org/proceedings/1997/215.md","bibtex":"https://ijcai.org/proceedings/1997/bibtex/215","volume":{"slug":"1997","title":"Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1997","url":"https://ijcai.org/proceedings/1997/","year":1997,"location":"Nagoya, Japan","dates":"","editors":"Martha E. Pollack","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Vision","track":"Vision","number":215,"pages":{"first":1496,"last":1500},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}