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

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 computing device 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. The papers hide all facets of desktop imaginative and prescient and trend acceptance resembling 3D computing device imaginative and prescient; computational images, sensing and show; face and gesture; low-level imaginative and prescient and photograph processing; movement and monitoring; optimization equipment; physics-based imaginative and prescient, photometry and shape-from-X; popularity: detection, categorization, indexing, matching; segmentation, grouping and form illustration; statistical equipment and studying; video: occasions, actions and surveillance; purposes. they're geared up in topical sections on detection, reputation and retrieval; scene knowing; optimization; photograph and video processing; studying; motion task and monitoring; 3D; and nine poster sessions.

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Extra info for Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II

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Int. J. Comput. Vis. 1(4), 333–356 (1988) 34. : 3D shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912–1920 (2015) 35. 0: a cloud-based network of 3D objects for robust grasp planning using a multi-armed bandit model with correlated rewards 36. : Real-time grasp detection using convolutional neural networks (2014). 3128 37. : Learning contact-rich manipulation skills with guided policy search (2015).

Aeroplane), and we aim to annotate the location of common object Fig. 1. An overview of our image co-localization framework. (a) The input of our system is a set of images contains a common object category (here, aeroplane). (b) The common object detector is learnt by modeling the distribution of detection confidence scores. (c) Detection heat maps generated by the learnt detector are used as the unary potential for graph-cuts segmentation. (d) The output for each image is the smallest rectangle which covers the corresponding segmentation.

Int. J. Comput. Vis. 1(4), 333–356 (1988) 34. : 3D shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912–1920 (2015) 35. 0: a cloud-based network of 3D objects for robust grasp planning using a multi-armed bandit model with correlated rewards 36. : Real-time grasp detection using convolutional neural networks (2014). 3128 37. : Learning contact-rich manipulation skills with guided policy search (2015).

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