Download PDF by Tatiana Tommasi, Novi Quadrianto, Barbara Caputo, Christoph: Computer Vision – ACCV 2012: 11th Asian Conference on

By Tatiana Tommasi, Novi Quadrianto, Barbara Caputo, Christoph H. Lampert (auth.), Kyoung Mu Lee, Yasuyuki Matsushita, James M. Rehg, Zhanyi Hu (eds.)

ISBN-10: 3642373305

ISBN-13: 9783642373305

ISBN-10: 3642373313

ISBN-13: 9783642373312

The four-volume set LNCS 7724--7727 constitutes the completely refereed post-conference lawsuits of the eleventh Asian convention on desktop imaginative and prescient, ACCV 2012, held in Daejeon, Korea, in November 2012. the complete of 226 contributions offered in those volumes used to be conscientiously reviewed and chosen from 869 submissions. The papers are geared up in topical sections on item detection, studying and matching; item popularity; function, illustration, and popularity; segmentation, grouping, and type; picture illustration; picture and video retrieval and scientific photograph research; face and gesture research and popularity; optical circulation and monitoring; movement, monitoring, and computational images; video research and motion attractiveness; form reconstruction and optimization; form from X and photometry; functions of desktop imaginative and prescient; low-level imaginative and prescient and functions of computing device vision.

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Additional resources for Computer Vision – ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part I

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In general, the cost of data labeling is quite expensive. To reduce the labeling effort, transfer learning [6] has been demonstrated to be a promising technique for object recognition with few training samples. It delivers useful knowledge in the source to improve the target model learning. However, most transfer learning algorithms [7,8,9] handle merely the knowledge delivery from either multiple classes or a single class in the source to a single class in the target, and assume high correlation between the source and target domains.

Takashi Shibata, Akihiko Iketani, and Shuji Senda 401 Image Super-Resolution: Use of Self-learning and Gabor Prior . . . . . Nilay Khatri and Manjunath V. Joshi 413 Oral Session 7: Video Analysis and Action Recognition Action Disambiguation Analysis Using Normalized Google-Like Distance Correlogram . . . . . . . . . . . . . . . . . . . . . . Qianru Sun and Hong Liu 425 Alpha-Flow for Video Matting . . . . . . . . . . .

Here, we show that the same solvers can still be used for our constrained problem as the linearity and additive model assumptions allow us to write d2n (xni , xnj ) = (Ln + Ls )(φ(xni ) − φ(xnj )) = Ln (φ(xni ) − φ(xnj )) 2 2 2 (4) 2 + Ls (φ(xni ) − φ(xnj )) 2 2 , (5) 8 T. Tommasi et al. and its analogous for d2t (xti , xtj ). The last equality follows directly from our or2 thogonality assumptions. Note that Ls (φ(xti ) − φ(xtj )) is fixed for each set 2 of neighbors and thus can be pre-computed.

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Computer Vision – ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part I by Tatiana Tommasi, Novi Quadrianto, Barbara Caputo, Christoph H. Lampert (auth.), Kyoung Mu Lee, Yasuyuki Matsushita, James M. Rehg, Zhanyi Hu (eds.)


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