Download Computer Vision – ECCV 2012: 12th European Conference on by Aamer Zaheer, Maheen Rashid, Sohaib Khan (auth.), Andrew PDF

By Aamer Zaheer, Maheen Rashid, Sohaib Khan (auth.), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid (eds.)

The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed complaints of the twelfth eu convention on laptop imaginative and prescient, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers provided have been conscientiously reviewed and chosen from 1437 submissions. The papers are geared up in topical sections on geometry, second and 3D shapes, 3D reconstruction, visible popularity and category, visible gains and snapshot matching, visible tracking: motion and actions, types, optimisation, studying, visible monitoring and photo registration, photometry: lighting fixtures and color, and snapshot segmentation.

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Extra info for Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI

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Their method based on using a mean 3D shape. Facial 3D geometry either can be acquired using 3D sensing devices such as laser scanners [12] or reconstructed from one or more images [13]. Although using 3D sensing devices has proven to be effective in 3D face recognition [14], their high cost and limited availability, appropriate only at close ranges and controlled environment. This has created the need for 3D face reconstruction methods to enable the extraction of 3D information from 2D acquired facial images.

Employing Pearson’s sample correlation, this demand translates into minimizing i∈Ip rp = w1T xi − w1T Xp w2T xi − w2T Xp (n − 1)s1 s2 = = w1T Xp − Xp , w2T Xp − Xp w1T Xp − Xp w2T Xp − Xp where Ip is the set of indices of the positive examples, Xp is a matrix whose columns are the mean vector of the positive class, wiT Xp and si for i = {1, 2} are the mean and standard deviation of wiT Xp , respectively. T w T (Xp −Xp ) Let yˆpi be the normalized predictions of wi on Xp , that is yˆpi = wi T X −X .

Let αi be the dual variable of the margin constraint of example (xi , yi ) and βi the dual variable of the non-negativity constraint of ξi . The Lagrangian of optimization function (Eq. 1) is L(w, α, β) = λ w 2 m m ξi + + i=1 k−1 2 + ηj wT vpj 2 + wT vnj 2 j=1 αi (1 − ξi − yi wT xi ) − i=1 m βi ξi . i=1 The w that minimizes the primal problem can be expressed as a function of the dual variables α and β by solving ∂L/∂w = 0, ⎛ ⎞−1 w = ⎝λI + k−1 j=1 ηj vpj vpTj + vnj vnTj ⎠ m αi yi xTi . i=1 Denote by V the matrix whose columns are the vectors vpi and vni multiplied by the square root of the matching tradeoff parameter ηi , √ √ √ √ (2) η1 vp1 , η1 vn1 , .

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