Download e-book for kindle: Computer Vision -- ACCV 2014: 12th Asian Conference on by Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang

By Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang

ISBN-10: 3319168169

ISBN-13: 9783319168166

ISBN-10: 3319168177

ISBN-13: 9783319168173

The five-volume set LNCS 9003--9007 constitutes the completely refereed post-conference lawsuits of the twelfth Asian convention on laptop imaginative and prescient, ACCV 2014, held in Singapore, Singapore, in November 2014.
The overall of 227 contributions provided in those volumes was once rigorously reviewed and chosen from 814 submissions. The papers are equipped in topical sections on popularity; 3D imaginative and prescient; low-level imaginative and prescient and lines; segmentation; face and gesture, monitoring; stereo, physics, video and occasions; and poster periods 1-3.

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Read or Download Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part IV PDF

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Extra info for Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part IV

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IEEE Trans. Image Process. 23, 1937–1952 (2014) 12. : Learning to predict where people look. In: ICCV (2009) 13. : Rare 2012: A multi-scale rarity-based saliency detection with its comparative statistical analysis. Sig. Process. Image Commun. 28, 642–658 (2013) 14. : An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. PAMI 26, 1124–1137 (2004) 15. : Methods for comparing scanpaths and saliency maps: strengths and weaknesses. Behav. Res. Method 1, 1–16 (2012) 16.

Yn ) are projections of video frames on the manifold of the graph path Pn . Obtaining y is equivalent to calculating the eigenvectors of the Laplacian graph of Pn such that it has eigenvectors y1 , y2 , . . , yn−1 . Linear extension of graph embedding [14] allows finding linear projection w from zeromean vectorized image x such that the objective function (2) is satisfied. (wT xi − wT xj )2 Wi,j , arg min w i, j = 1, 2, . . , n (3) i,j He et al. [15] solves the resulting eigenvalue problem XLXT w = λ XXT w (4) by using the singular value decomposition with X = UΣVT .

The main goal is to compute the weighting coefficients in order to improve the degree of similarity between the ground truth and the aggregated saliency map. These weights are computed thanks to the following methods: 1 ; – Uniform: weights w are uniform and spatially invariant, wk = K – Median: weights w are locally deduced from the saliency values. All weights are null, except for the one which corresponds to the median value of the saliency values for a given location. In this case, weights are spatially variant; Saliency Aggregation: Does Unity Make Strength?

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Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part IV by Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang


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