Advances in Visual Computing: Third International Symposium, by Giorgio Panin, Alois Knoll (auth.), George Bebis, Richard

By Giorgio Panin, Alois Knoll (auth.), George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Nikos Paragios, Syeda-Mahmood Tanveer, Tao Ju, Zicheng Liu, Sabine Coquillart, Carolina Cruz-Neira, Torsten Müller, Tom Malzbender (eds.)

It is with nice excitement that we welcome you to the complaints of the third - ternational Symposium on visible Computing (ISVC 2007) held in Lake Tahoe, Nevada/California. ISVC o?ers a typical umbrella for the 4 major components of visualcomputing together with vision,graphics,visualization,andvirtualreality.Its target is to supply a discussion board for researchers, scientists, engineers and practitioners in the course of the international to offer their most recent examine ?ndings, rules, devel- ments, and functions within the broader sector of visible computing. Thisyear,theprogramconsistedof14oralsessions,1postersession,6special tracks, and six keynote displays. Following a truly profitable ISVC 2006, the reaction to the decision for papers was once virtually both powerful; we bought over 270 submissions for the most symposium from which we permitted seventy seven papers for oral presentation and forty two papers for poster presentation. targeted music papers have been solicited individually in the course of the Organizing and application Committees of every tune. a complete of 32 papers have been approved for oral presentation and five papers for poster presentation within the distinctive tracks.

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Additional info for Advances in Visual Computing: Third International Symposium, ISVC 2007, Lake Tahoe, NV, USA, November 26-28, 2007, Proceedings, Part I

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CVPR, New York, NY, USA, vol. 1, pp. 782–789 (2006) 9. : Human body model acquisition and tracking using voxel data. Trans. IJCV 53, 199–223 (2003) 10. : Tracking and modeling people in video sequences. Trans. Computer Vision and Image Understanding (CVIU) 81, 285–302 (2001) 11. : Smart particle filtering for high-dimensional tracking. Trans. CVIU 106, 116–129 (2007) 12. : Model-based 3D tracking of an articulated hand. In: Proc. CVPR, Kauai, HI, USA, vol. 2, pp. 310–315 (2001) 13. : Model-based hand tracking using a hierarchical bayesian filter.

The Observation Model is based on the difference between the sampled pixel grey level patch at the hypothesized position in the current image and the one generated by the face model. The likelihood function p(yk |xk ) denotes the probability that a hypothesized state xk = (ck , pk ) gives rise to the 16 L. Bagnato et al. observed data. Since the observed data consist of pixel greylevel values, it is straightforward to look for a function with the following form: p(yk |xk ) = p(yk |ck , pk ) = C exp (−d[gmodel (ck ), gimage (ck , pk )]) (3) where gimage (ck , pk ) is the image patch sampled at the hypothesized pose and shape, gmodel (ck ) is the model texture representing the hypothesized appearance of the face, and C is a normalizing constant.

In this case a proper dynamics is the one described in (6). e. maintaining multiple hypothesis, is constrained in a limited regionof the state space by the importance function. The two described approaches are complementary: the first one sacrifices accuracy for robustness, while the second one allows fast and accurate tracking, but only in occlusions free situations. The integration of both dynamics into the same tracker would then allow for a wider range of motion to be supported without losing the advantages of an accurate prediction.

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