Computer Vision – ECCV 2016: 14th European Conference, by Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling PDF

By Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

ISBN-10: 3319464833

ISBN-13: 9783319464831

ISBN-10: 3319464841

ISBN-13: 9783319464848

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed complaints of the 14th ecu convention on desktop imaginative and prescient, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016.
The 415 revised papers awarded have been rigorously reviewed and chosen from 1480 submissions. The papers hide all points of laptop imaginative and prescient and development attractiveness akin to 3D computing device imaginative and prescient; computational images, sensing and demonstrate; face and gesture; low-level imaginative and prescient and photo 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 equipment and studying; video: occasions, actions and surveillance; purposes. they're equipped in topical sections on detection, reputation and retrieval; scene figuring out; optimization; photo and video processing; studying; motion, task and monitoring; 3D; and nine poster sessions.

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Additional resources for Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII

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Thus it becomes crucial to design effective representations that can deal with these challenges while preserve categorical information of action classes. Recently, Convolutional Networks (ConvNets) [7] have witnessed great success in classifying images of objects, scenes, and complex events [8–11]. ConvNets have also been introduced to solve the problem of video-based action recognition [1,12–14]. Deep ConvNets come with great modeling capacity and are capable of learning discriminative representation from raw visual data with the help of large-scale supervised datasets.

2. IEEE (2006) 4. : Advances in video summarization and skimming. , Ghanbari, M. ) Recent Advances in Multimedia Signal Processing and Communications. SCI, vol. 231, pp. 27–50. Springer, Heidelberg (2009) 5. : Making a long video short: dynamic video synopsis. In: 2006 IEEE Computer Society Conference on CVPR, vol. 1. IEEE (2006) 6. : Schematic storyboarding for video visualization and editing. ACM Trans. Graph. (TOG) 25, 862–871 (2006). ACM 7. : Optimization algorithms for the selection of key frame sequences of variable length.

28] proposed a factorized spatiotemporal ConvNets and exploited different ways to decompose 3D convolutional kernels. Recently, several works focused on modeling long-range temporal structure with ConvNets [4,19,20]. However, these methods directly operated on a longer continuous video streams. Limited by computational cost these methods usually process sequences of fixed lengths ranging from 64 to 120 frames. It is non-trivial for these methods to learn from entire video due to their limited temporal coverage.

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Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII by Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling


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