Computer Vision – ECCV 2014: 13th European Conference, by David Fleet, Tomas Pajdla, Bernt Schiele, Tinne Tuytelaars

By David Fleet, Tomas Pajdla, Bernt Schiele, Tinne Tuytelaars

The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed court cases of the thirteenth ecu convention on computing device imaginative and prescient, ECCV 2014, held in Zurich, Switzerland, in September 2014.

The 363 revised papers provided have been conscientiously reviewed and chosen from 1444 submissions. The papers are geared up in topical sections on monitoring and job attractiveness; popularity; studying and inference; constitution from movement and have matching; computational images and low-level imaginative and prescient; imaginative and prescient; segmentation and saliency; context and 3D scenes; movement and 3D scene research; and poster sessions.

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Additional info for Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III

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2 Generating Candidate Placements In this section we describe how to generate the set of candidate placements of 3D pieces to map points of interest. First, we parse the map into a set of regions The 3D Jigsaw Puzzle: Mapping Large Indoor Spaces 7 Fig. 3. Left: A 3D piece of our system corresponding to the Hall of the Immaculate Conception. Middle: Colored 2D regions extracted from the floorplan. Number 72 in purple corresponds to the ground truth location of the 3D piece. Right: Candidate placements of the 3D piece to the 2D region.

Fleet et al. ): ECCV 2014, Part III, LNCS 8691, pp. 17–30, 2014. c Springer International Publishing Switzerland 2014 18 R. -Y. Zhou, and U. , [11,24,23]). This strategy is well-suited to industrial sites since most parts of them are composed of primitive shapes ([12,20,21]). However, such methods rarely extract complete pipe-runs with accurate connectivity. Moreover, bottom-up primitive fitting adopted in these methods is non-robust due to sensitivity to noise and outliers (Figure 2(e)). , Figure 1).

2963–2970 (2010) 29. : Multi-digit number recognition from street view imagery using deep convolutional neural networks. 6082 (2013) 30. : Photoocr: Reading text in uncontrolled conditions. In: The IEEE International Conference on Computer Vision (ICCV) (December 2013) 31. : Labelme: A database and web-based tool for image annotation. Int. J. Comput. Vision 77(1-3), 157–173 (2008) Pipe-Run Extraction and Reconstruction from Point Clouds Rongqi Qiu1 , Qian-Yi Zhou2 , and Ulrich Neumann1, 1 University of Southern California, USA 2 Stanford University, USA Abstract.

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