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 lawsuits of the third - ternational Symposium on visible Computing (ISVC 2007) held in Lake Tahoe, Nevada/California. ISVC o?ers a standard umbrella for the 4 major components of visualcomputing together with vision,graphics,visualization,andvirtualreality.Its aim is to supply a discussion board for researchers, scientists, engineers and practitioners in the course of the global to give their most modern examine ?ndings, principles, devel- ments, and functions within the broader quarter of visible computing. Thisyear,theprogramconsistedof14oralsessions,1postersession,6special tracks, and six keynote displays. Following a truly winning ISVC 2006, the reaction to the decision for papers was once virtually both powerful; we acquired over 270 submissions for the most symposium from which we accredited seventy seven papers for oral presentation and forty two papers for poster presentation. certain tune papers have been solicited individually in the course of the Organizing and application Committees of every song. a complete of 32 papers have been approved for oral presentation and five papers for poster presentation within the specific tracks.
Read or Download Advances in Visual Computing: Third International Symposium, ISVC 2007, Lake Tahoe, NV, USA, November 26-28, 2007, Proceedings, Part I PDF
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Extra info for Advances in Visual Computing: Third International Symposium, ISVC 2007, Lake Tahoe, NV, USA, November 26-28, 2007, Proceedings, Part I
1(b). 4 Projection Model Under the assumption of pin-hole cameras we use perspective projection with our 3D Corpulence model C(x) to define corresponding conic fields on the image plane onto which each of its quadric fields will project. These conic fields are defined through their conic envelopes C ∗b : C ∗b (x) = PQ ∗b (x)PT (15) Gradient-Based Hand Tracking Using Silhouette Data 29 where P is the 3 × 4 camera projection matrix that holds both intrinsic and extrinsic calibration parameters for each of the cameras, and Q ∗b (x) is the envelope of the statetransformed quadric field associated with ellipsoid b .
Regression-based hand pose estimation from multiple cameras. In: Proc. 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.
In this spirit we opted for a mixed-state framework with model switching. 1 Pose-CONDENSATION and ICONDENSATION The ﬁrst two retained models consist in a ﬁxed constant-velocity model with ﬁxed noise variance (5) xk = xk−1 + Sk u; and an adaptive dynamic model, guided by a deterministic AAM search: xk = xk−1 + Δxk−1,k + Sk u, (6) where Δxk−1,k = (Δck−1,k , Δpk−1,k ) is the predicted shift in pose and appearance parameters, obtained by applying the AAM search to previous estimated state vector (xk−1 ) with respect to frame at time k.