Computer Vision - ACCV 2014 Workshops: Singapore, Singapore, by C.V. Jawahar, Shiguang Shan

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By C.V. Jawahar, Shiguang Shan

The three-volume set, which includes LNCS 9008, 9009, and 9010, includes rigorously reviewed and chosen papers provided at 15 workshops held at the side of the twelfth Asian convention on desktop imaginative and prescient, ACCV 2014, in Singapore, in November 2014. The 153 complete papers awarded have been chosen from quite a few submissions. LNCS 9008 includes the papers chosen for the Workshop on Human Gait and motion research within the Wild, the second one overseas Workshop on giant info in 3D machine imaginative and prescient, the Workshop on Deep studying on visible information, the Workshop on Scene figuring out for self reliant platforms and the Workshop on powerful neighborhood Descriptors for machine imaginative and prescient. LNCS 9009 includes the papers chosen for the Workshop on rising subject matters on picture recovery and Enhancement, the 1st overseas Workshop on strong analyzing, the second one Workshop on User-Centred desktop imaginative and prescient, the overseas Workshop on Video Segmentation in computing device imaginative and prescient, the Workshop: My motor vehicle Has Eyes: clever automobile with imaginative and prescient expertise, the 3rd Workshop on E-Heritage and the Workshop on laptop imaginative and prescient for Affective Computing. LNCS 9010 includes the papers chosen for the Workshop on characteristic and Similarity for desktop imaginative and prescient, the 3rd overseas Workshop on clever cellular and selfish imaginative and prescient and the Workshop on Human identity for Surveillance.

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Additional info for Computer Vision - ACCV 2014 Workshops: Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part I

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As expected, the performance of the global Gauss map (1 box) is the lowest. Next, we vary the number of histogram bins (8, 16, 32) for a fixed grid of 36 boxes. The results are shown in Fig. 7. Further performance enhancements are achievable but cannot be shown at the used scale. Instead, we use the Equal Error Rate (EER) value from the ROC curve, that is the value at which the false acceptance and rejection rates (FAR and FRR) are equal. We also use the rank-1 and rank-5 identification rates as representative measures from the CMC curve.

For each detected upper body, its score is redefined as the ratio of regions which are supposed to be hands. These regions should, first, be in motion, and second, be not too large or too small compared to the upper body. We show these regions by the red bounding boxes. The detection at the right side contains no motion, thus it is not supposed to be the good one. (1b) The middle detection is considered to be better than the left one since it contains more motion based hand hypotheses. Upper bodies and faces are marked by yellow and light blue bounding boxes respectively.

30, 212–221 (2009) 39. : Improved gait recognition using gradient histogram energy image. In: 19th IEEE International Conference on Image Processing (ICIP), pp. 1389–1392. IEEE (2012) 40. : Exploiting gradient histograms for gait-based person identification. In: IEEE International Conference on Image Processing (ICIP), pp. 4171–4175 (2013) 41. : Two-point gait: decoupling gait from body shape. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1041–1048. IEEE (2013) 42. : Style adaptive contour tracking of human gait using explicit manifold models.

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