By Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed court cases of the 14th eu convention on desktop imaginative and prescient, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016.
The 415 revised papers provided have been rigorously reviewed and chosen from 1480 submissions. The papers disguise all facets of desktop imaginative and prescient and trend acceptance equivalent to 3D computing device imaginative and prescient; computational images, sensing and reveal; face and gesture; low-level imaginative and prescient and photograph processing; movement and monitoring; optimization tools; physics-based imaginative and prescient, photometry and shape-from-X; reputation: detection, categorization, indexing, matching; segmentation, grouping and form illustration; statistical tools and studying; video: occasions, actions and surveillance; purposes. they're equipped in topical sections on detection, popularity and retrieval; scene realizing; optimization; snapshot and video processing; studying; motion, task and monitoring; 3D; and nine poster sessions.
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Extra resources for Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII
E 51(5), 4282 (1995) 3. : Abnormal crowd behavior detection using social force model. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009. IEEE, pp. 935–942 (2009) 4. : A modiﬁed social force model for pedestrian behavior simulation at signalized crosswalks. Procedia Soc. Behav. Sci. 138, 521– 530 (2014) 5. : A modiﬁcation of the social force model can reproduce experimental data of pedestrian ﬂows in normal conditions. Phys. A 388(17), 3600–3608 (2009) 6. : Social force model with explicit collision prediction.
This experiment (the real time eﬃciency of BOW encoding is shown in ). First, we measured the relative computational time of our method with respect to violent-ﬂows . Figure 6(a) shows the ratio between time to process a clip for VIPS and violent-ﬂows as a function of number of sampled patches and video resolution. For both methods we employed the same implementation of the optical ﬂow in . 5 to 2 times slower compared to . During the experiments, we observed that the dominant computational cost of our method belongs to the optical ﬂow computation, in particular for medium-to-high resolutions, whereas the convolutions (in the frequency domain) add negligible computational burden.
Hence, a natural way to represent spectra observed in natural images is a sparse combination of basis spectra stored in a dictionary. Indeed, among several methods proposed in the ﬁeld of color science for reﬂectance estimation from RGB images , regression estimation suggests the use of a dictionary containing a collection of reﬂectance/measurement pairs in order to estimate the underlying reﬂectance of new measurements. While previous studies [21,36,37] have attempted to apply the regression estimation method for reﬂection estimation, most of them were limited to theoretical studies on small datasets of known “generic” spectra (such as the Munsell color chip set) or to domain speciﬁc tasks .