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 desktop 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 points of computing device imaginative and prescient and development popularity equivalent to 3D computing device imaginative and prescient; computational images, sensing and reveal; 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; attractiveness: detection, categorization, indexing, matching; segmentation, grouping and form illustration; statistical equipment and studying; video: occasions, actions and surveillance; functions. they're prepared in topical sections on detection, acceptance and retrieval; scene figuring out; optimization; picture and video processing; studying; motion, job and monitoring; 3D; and nine poster sessions.

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

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For many popular networks this is equal to 256 or 512, which makes it a compact image representation. MAC vectors are subsequently 2 normalized and similarity between two images is evaluated with inner product. The contribution of a feature map to the image similarity is measured by the product of the corresponding MAC vector components. In Fig. 1 we show the image patches in correspondence that contribute most to the similarity. Such implicit correspondences are improved after fine-tuning. g. cars and bicycles.

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.

Furthermore, significantly improving the speed of high-quality detection can broaden the range of settings where computer vision is useful. We summarize our contributions as follows: – We introduce SSD, a single-shot detector for multiple categories that is faster than the previous state-of-the-art for single shot detectors (YOLO), and significantly more accurate, in fact as accurate as slower techniques that perform explicit region proposals and pooling (including Faster R-CNN). – The core of SSD is predicting category scores and box offsets for a fixed set of default bounding boxes using small convolutional filters applied to feature maps.

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