Computer Vision Pattern Recognition

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 complaints of the thirteenth eu convention on laptop imaginative and prescient, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers provided have been rigorously reviewed and chosen from 1444 submissions. The papers are equipped in topical sections on monitoring and task attractiveness; attractiveness; 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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Extra info for Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI

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24 Q. Shan et al. (a) (b) (c) (d) Fig. 3. Ground truth experiment (San Peter Cathedral). (a) The ground truth image. (b) We only keep 1/9 of the image in the center, which is the input to our system. (c) Uncropped to the ground truth image size. The ground truth image in (a) was not used in creating this composite. (d) Uncropped to even wider FOV than the original. Next, we forward-warp the remaining source images into the target image using splatting and a soft Z-buffer algorithm. We project each source image pixel into the target view, eliminating source pixels that are backfacing to the target view.

Our goal is to see through the occluder in the front and image for all others continuously. Figure 6 shows the comparison result of our method and Vaish et al [11]. In frame#210, the man in saffron cloth is occluded. In Vaish’s result(Figure 6(b)), the occluded man is blurred by shadows from the occluder. In addition, people out of the focus plane are all blurred. In contrast, our approach could see 12 T. Yang et al. (a) Reference view  a b c (b) Vaish et al.  d  a (d) Our method (c) Pei et al.

573–583. Springer, Heidelberg (2009) 22. : Accurate, dense, and robust multi-view stereopsis. TPAMI 32(8), 1362–1376 (2010) Photo Uncrop Qi Shan1 , Brian Curless1 , Yasutaka Furukawa2 , Carlos Hernandez3, and Steven M. Seitz1,3 2 1 University of Washington, Seattle, WA, USA Washington University in St. Louis, St. , Mountain View, CA, USA Abstract. We address the problem of extending the field of view of a photo— an operation we call uncrop. Given a reference photograph to be uncropped, our approach selects, reprojects, and composites a subset of Internet imagery taken near the reference into a larger image around the reference using the underlying scene geometry.

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