We give a formal definition of geometric fitting in a way that suits computer vision applications. We point out that the performance of geometric fitting should be evaluated in the limit of small noise rather than in the limit of a large number of data as recommended in the statistical literature. Taking the KCR lower bound as an optimality requirement and focusing on the linearized constraint case, we compare the accuracy of Kanatani's renormalization with maximum likelihood (ML) approaches including the FNS of Chojnacki et al. and the HEIV of Leedan and Meer. Our analysis reveals the existence of a method superior to all these.
en-copyright= kn-copyright= en-aut-name=KanataniKenichi en-aut-sei=Kanatani en-aut-mei=Kenichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= affil-num=1 en-affil= kn-affil=Department of Computer Science, Okayama University en-keyword=computer vision kn-keyword=computer vision en-keyword=maximum likelihood estimation kn-keyword=maximum likelihood estimation en-keyword=surface fitting kn-keyword=surface fitting END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page=118 end-page=125 dt-received= dt-revised= dt-accepted= dt-pub-year=2005 dt-pub=20056 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Coordination of appearance and motion data for virtual view generation of traditional dances en-subtitle= kn-subtitle= en-abstract= kn-abstract=A novel method is proposed for virtual view generation of traditional dances. In the proposed framework, a traditional dance is captured separately for appearance registration and motion registration. By coordinating the appearance and motion data, we can easily control virtual camera motion within a dancer-centered coordinate system. For this purpose, a coordination problem should be solved between the appearance and motion data, since they are captured separately and the dancer moves freely in the room. The present paper shows a practical algorithm to solve it. A set of algorithms are also provided for appearance and motion registration, and virtual view generation from archived data. In the appearance registration, a 3D human shape is recovered in each time from a set of input images after suppressing their backgrounds. By combining the recovered 3D shape and a set of images for each time, we can compose archived dance data. In the motion registration, stereoscopic tracking is accomplished for color markers placed on the dancer. A virtual view generation is formalized as a color blending among multiple views, and a novel and efficient algorithm is proposed for the composition of a natural virtual view from a set of images. In the proposed method, weightings of the linear combination are calculated from both an assumed viewpoint and a surface normal.
en-copyright= kn-copyright= en-aut-name=KamonYuji en-aut-sei=Kamon en-aut-mei=Yuji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=YamaneRyo en-aut-sei=Yamane en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=MukaigawaYasuhiro en-aut-sei=Mukaigawa en-aut-mei=Yasuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=ShakunagaTakeshi en-aut-sei=Shakunaga en-aut-mei=Takeshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil= kn-affil=Sharp Corporation affil-num=2 en-affil= kn-affil=Okayama University affil-num=3 en-affil= kn-affil=Osaka University affil-num=4 en-affil= kn-affil=Okayama University en-keyword=humanities kn-keyword=humanities en-keyword=image colour analysis kn-keyword=image colour analysis en-keyword=image motion analysis kn-keyword=image motion analysis en-keyword=image registration kn-keyword=image registration en-keyword=stereo image processing kn-keyword=stereo image processing en-keyword=tracking kn-keyword=tracking en-keyword=virtual reality kn-keyword=virtual reality END