By Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed lawsuits of the 14th eu convention on desktop imaginative and prescient, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016.
The 415 revised papers offered have been rigorously reviewed and chosen from 1480 submissions. The papers disguise all facets of desktop imaginative and prescient and trend popularity akin to 3D machine imaginative and prescient; computational images, sensing and show; face and gesture; low-level imaginative and prescient and snapshot processing; movement and monitoring; optimization equipment; physics-based imaginative and prescient, photometry and shape-from-X; acceptance: 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, reputation and retrieval; scene figuring out; optimization; snapshot and video processing; studying; motion, job and monitoring; 3D; and nine poster sessions.
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The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed lawsuits of the 14th ecu convention on laptop imaginative and prescient, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers awarded have been conscientiously reviewed and chosen from 1480 submissions.
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Extra resources for Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII
By illuminating a target scene with several narrow-band light sources, a process known as “timemultiplexed illumination”, scene reﬂectance can be estimated across a number of wavelengths. Goel et al.  proposed such a system capable of estimating 17 spectral bands at 9 fps using time multiplexed illumination, while Parmar et al.  demonstrated the recovery of 31 spectral bands using 5 narrow-band LED sources. This approach seemingly removes computational and temporal hurdles faced by previous eﬀorts but introduces a new constraint of controlled lighting, thus rendering itself ineﬀective in outdoor conditions, large scale environments or conditions where illumination changes are prohibited.
In particular, VIC is the only available dataset speciﬁcally assembled for classifying acts of violence in crowd scenes, while VIM allows us to evaluate the robustness of our approach in person-on-person violent scenes. We also select 3 To employ K-means, we rasterize each patch in a vector of length 125 along with the Euclidean distance. , 2000]. 12 S. Mohammadi et al. Fig. 3. First three columns are frame samples taken from Violence in Crowds (VIC), Violence in Movies (VIM), BEHAVE and Violence-Cross (VC) datasets, respectively.
93(6), 68005 (2011) 7. : How simple rules determine pedestrian behavior and crowd disasters. Proc. Natl. Acad. Sci. 108(17), 6884–6888 (2011) 8. : Simple heuristics and the modelling of crowd behaviours. , Schreckenberg, M. ) Pedestrian and Evacuation Dynamics 2012, pp. 75–90. Springer, Heidelberg (2014) 9. : Traﬃc instabilities in self-organized pedestrian crowds. PLoS Comput. Biol. 8(3), e1002442 (2012) 10. : Fast, frugal, and ﬁt: Simple heuristics for paired comparison. Theor. Decis. 52(1), 29–71 (2002) 11.