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: 3319464477

ISBN-13: 9783319464473

ISBN-10: 3319464485

ISBN-13: 9783319464480

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed complaints of the 14th ecu convention on machine 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 conceal all facets of laptop imaginative and prescient and development reputation reminiscent of 3D computing device imaginative and prescient; computational images, sensing and exhibit; face and gesture; low-level imaginative and prescient and snapshot processing; movement and monitoring; optimization tools; physics-based imaginative and prescient, photometry and shape-from-X; attractiveness: 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, reputation and retrieval; scene realizing; 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 I

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We use the terms 3D model, model and cluster interchangeably. For each image, the estimated camera position is known, as well as the local features registered on the 3D model. We drop redundant (overlapping) 3D models, that might have been constructed from different seeds. Models reconstructing the same landmark but from different and disjoint viewpoints are considered as non-overlapping. 2 Selection of Training Image Pairs A 3D model is described as a bipartite visibility graph G = (I ∪ P, E) [55], where images I and points P are the vertices of the graph.

A concurrent work [36] bears resemblance to ours but their focus is on boosting performance through end-to-end learning of a more sophisticated representation, while we target to reveal the importance of hard examples and of training data variation. A number of image clustering methods based on local features have been introduced [37–39]. Due to the spatial verification, the clusters discovered by these methods are reliable. In fact, the methods provide not only clusters, but also a matching graph or sub-graph on the cluster images.

Cross-dimensional weighting for aggregated deep convolutional features. 04065 (2015) 25. : Particular object retrieval with integral maxpooling of CNN activations. In: ICLR (2016) 26. : From generic to specific deep representations for visual recognition. In: CVPRW (2015) 27. : Part-based R-CNNs for finegrained category detection. , Tuytelaars, T. ) ECCV 2014. LNCS, vol. 8689, pp. 834–849. Springer, Heidelberg (2014). 1007/978-3-319-10590-1 54 28. : Learning and transferring mid-level image representations using convolutional neural networks.

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

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