Get Computer Vision – ECCV 2016: 14th European Conference, PDF

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

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed court cases of the 14th eu convention on desktop imaginative and prescient, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016.
The 415 revised papers provided have been conscientiously reviewed and chosen from 1480 submissions. The papers disguise all points of desktop imaginative and prescient and trend acceptance similar to 3D machine imaginative and prescient; computational images, sensing and show; face and gesture; low-level imaginative and prescient and photograph 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; functions. they're geared up in topical sections on detection, popularity and retrieval; scene realizing; optimization; snapshot and video processing; studying; motion, job and monitoring; 3D; and nine poster sessions.

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Read or Download Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I PDF

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

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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. – To achieve high detection accuracy we produce predictions of different scales from feature maps of different scales, and explicitly separate predictions by aspect ratio. SSD: Single Shot MultiBox Detector 23 Fig. 1. SSD framework. (a) SSD only needs an input image and ground truth boxes for each object during training. g. g. 8×8 and 4×4 in (b) and (c)).

Distinctive image features from scale-invariant keypoints. IJCV 60, 91– 110 (2004) 2. : Video Google: A text retrieval approach to object matching in videos. In: ICCV (2003) 3. : Object retrieval with large vocabularies and fast spatial matching. In: CVPR (2007) 4. : Approximate Gaussian mixtures for large scale vocabularies. , Schmid, C. ) ECCV 2012. LNCS, vol. 7574, pp. 15–28. Springer, Heidelberg (2012). 1007/ 978-3-642-33712-3 2 5. : Spatially-constrained similarity measure for large-scale object retrieval.

Schmid, C. ) ECCV 2012. LNCS, vol. 7574, pp. 15–28. Springer, Heidelberg (2012). 1007/ 978-3-642-33712-3 2 5. : Spatially-constrained similarity measure for large-scale object retrieval. PAMI 36, 1229–1241 (2014) 6. : Total recall II: query expansion revisited. In: CVPR (2011) 7. : Hello neighbor: accurate object retrieval with k-reciprocal nearest neighbors. In: CVPR (2011) 8. : Visual query expansion with or without geometry: refining local descriptors by feature aggregation. Pattern Recogn.

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