By Xiaoyi Jiang, Joachim Hornegger, Reinhard Koch
This booklet constitutes the refereed complaints of the thirty sixth German convention on development popularity, GCPR 2014, held in Münster, Germany, in September 2014. The fifty eight revised complete papers and eight brief papers have been conscientiously reviewed and chosen from 153 submissions. The papers are equipped in topical sections on variational versions for intensity and stream, reconstruction, bio-informatics, deep studying and segmentation, function computation, video interpretation, segmentation and labeling, photo processing and research, human pose and folks monitoring, interpolation and inpainting.
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Additional resources for Pattern Recognition: 36th German Conference, GCPR 2014, Münster, Germany, September 2-5, 2014, Proceedings
Variational deconvolution of multi-channel images with inequality constraints. , Serrat, J. ) IbPRIA 2007. LNCS, vol. 4477, pp. 386–393. Springer, Heidelberg (2007) 29. : A new method of graduation. Proc. Edinburgh Math. Soc. edu German Aerospace Center, Oberpfaﬀenhofen, Germany 3 Reykjavik University, Reykjavik, Iceland 4 LiveRamp, San Francisco, USA Abstract. We present a structured lighting system for creating highresolution stereo datasets of static indoor scenes with highly accurate ground-truth disparities.
Springer, Heidelberg (2010) 11. : Dense 3d motion capture from synchronized video streams. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008). IEEE Computer Society, Anchorage, 24–26 June 2008 12. : The variational structure of disparity and regularization of 4d light ﬁelds. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013) 13. : The lumigraph. In: SIGGRAPH, pp. 43–54 (1996) 14. : Variational shape from light ﬁeld. In: International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (2013) 15.
38 D. Scharstein et al. Fig. 6. Reduction of maximum (left) and average (right) absolute vertical rectiﬁcation errors through our novel calibration reﬁnement technique over all 33 datasets. Self-calibration of projectors: So far we have only utilized the interpolated code values (u, v) as unique “scene color” allowing us to establish unambiguous subpixel correspondences between the two cameras. But since (u, v) also encodes the pixel location in the projector’s illumination pattern, we get additional correspondences for each projector-camera pair.