Download Computer Vision – ECCV 2008: 10th European Conference on by Saad Ali, Mubarak Shah (auth.), David Forsyth, Philip Torr, PDF

By Saad Ali, Mubarak Shah (auth.), David Forsyth, Philip Torr, Andrew Zisserman (eds.)

ISBN-10: 3540886850

ISBN-13: 9783540886853

The four-volume set comprising LNCS volumes 5302/5303/5304/5305 constitutes the refereed lawsuits of the tenth eu convention on laptop imaginative and prescient, ECCV 2008, held in Marseille, France, in October 2008.

The 243 revised papers offered have been conscientiously reviewed and chosen from a complete of 871 papers submitted. The 4 books disguise the full diversity of present concerns in desktop imaginative and prescient. The papers are equipped in topical sections on acceptance, stereo, humans and face acceptance, item monitoring, matching, studying and lines, MRFs, segmentation, computational images and lively reconstruction.

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Extra resources for Computer Vision – ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part II

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Of the 3rd International Workshop on Particle Image Velocimetry (1999) 16. : Mean Shift: A Robust Approach Toward Feature Space Analysis. In: IEEE TPAMI (2002) The Naked Truth: Estimating Body Shape Under Clothing Alexandru O. B˘ alan and Michael J. edu Abstract. We propose a method to estimate the detailed 3D shape of a person from images of that person wearing clothing. The approach exploits a model of human body shapes that is learned from a database of over 2000 range scans. We show that the parameters of this shape model can be recovered independently of body pose.

Specifically we add a penalty on the shape coefficients βi of the form Eshape (β) = max 0, j |βj | − σthresh σβ,j 2 . (6) No penalty is paid for small values of β and we only start penalizing shape coefficients when they exceed σthresh = 3 standard deviations from the mean. We also enforce a prior, Epose (θ), on body pose which is uniform within joint angle limits and only penalizes poses beyond these limits. Finally, the objective function we minimize is P K Eclothes (β, Θ) = p=1 k=1 Einside (β, θp ) Eexpand (β, θp ) + + Eshape (β) + Epose (θp ) σo2 σo2 where Θ = θ1 , .

G. [16]), but in many situations the face may be too small for reliable classification. g. [17]). Surprisingly, this work typically takes silhouettes and extracts information about gait while throwing away the body shape information that can provide direct evidence about gender. We believe ours is the first method to infer a parametric 3D human body shape from images of clothed people and to use it for gender classification. The Naked Truth: Estimating Body Shape Under Clothing 19 ··· ··· ··· Fig.

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