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 complaints 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 offered have been conscientiously reviewed and chosen from 1480 submissions. The papers hide all elements of machine imaginative and prescient and development acceptance corresponding to 3D machine 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; popularity: detection, categorization, indexing, matching; segmentation, grouping and form illustration; statistical equipment and studying; video: occasions, actions and surveillance; purposes. they're prepared in topical sections on detection, acceptance and retrieval; scene figuring out; 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 IV

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3D pose estimation from a single image is an inherently ill-posed problem due to the possibility of multiple plausible projections from the same 2D image to a variety of 3D poses. A common approach is to project the estimated 2D landmarks from single image to the 3D space by imposing certain constraints [5,9,10,22,23]. Simo-Serra et al. [10] imposed kinematic constraints to guarantee that the resulting pose resembles a human shape. In [5,9], 3D pose is represented as a sparse linear combination of an overcomplete dictionary.

We propose two baseline models: a description model and a definition model. Our description baseline generates sentences conditioned only on images and is equivalent to LRCN [7] except we use image features from a fine-grained classifier [10]. Our definition baseline generates sentences using only an image label as input. Consequently, this model outputs the same sentence for every image of the same class. Our proposed model is both more image and class relevant than either of these baselines and thus superior for the explanation task.

Show, attend and tell: neural image caption generation with visual attention. In: ICML (2015) 41. : Every moment counts: dense detailed labeling of actions in complex videos. sg Abstract. The recovery of 3D human pose with monocular camera is an inherently ill-posed problem due to the large number of possible projections from the same 2D image to 3D space. Aimed at improving the accuracy of 3D motion reconstruction, we introduce the additional builtin knowledge, namely height-map, into the algorithmic scheme of reconstructing the 3D pose/motion under a single-view calibrated camera.

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