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By Tal Hassner, Ce Liu

This publication describes the basic building-block of many new computing device imaginative and prescient platforms: dense and powerful correspondence estimation. Dense correspondence estimation options are actually effectively getting used to resolve a variety of machine imaginative and prescient difficulties, very varied from the conventional purposes such recommendations have been initially constructed to unravel. This publication introduces the options used for developing correspondences among tough picture pairs, the unconventional good points used to make those thoughts strong, and the various difficulties dense correspondences at the moment are getting used to resolve. The ebook offers info to a person trying to make the most of dense correspondences which will clear up new or latest desktop imaginative and prescient difficulties. The editors describe how you can clear up many laptop imaginative and prescient difficulties through the use of dense correspondence estimation. ultimately, it surveys assets, code and information, beneficial for expediting the advance of potent correspondence-based desktop imaginative and prescient systems.

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Dense Image Correspondences for Computer Vision

This ebook describes the elemental building-block of many new laptop imaginative and prescient structures: dense and strong correspondence estimation. Dense correspondence estimation options are actually effectively getting used to resolve quite a lot of computing device imaginative and prescient difficulties, very various from the normal functions such strategies have been initially built to unravel.

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Cosegmentation of image pairs by histogram matching – incorporating a global constraint into MRFs. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 993–1000 (2006) 42. : Object recognition by scene alignment. In: Advances in Neural Information Processing Systems (NIPS) (2007) 43. : LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1–3), 157–173 (2008) 44. : Parameterization of a stochastic model for human face identification.

We also demonstrated through many examples that scene alignment can be a very useful tool to many computer vision problems. 8 1 Ratio of samples used for training 92x112 Fig. 24 SIFT flow is applied for face recognition. The curves in (a) and (b) are the performance plots for low-res and high-res images in the ORL face database, respectively. SIFT flow significantly boosted the recognition rate especially when there are not enough training samples Table 1 Our face recognition system using SIFT flow is comparable with the state of the art [13] when there are only few (one or two) training samples Test errors S-LDA [13] SIFT flow 1 Train N/A 28:4 ˙ 3:0 2 Train 17:1 ˙ 2:7 16:6 ˙ 2:2 3 Train 8:1 ˙ 1:8 8:9 ˙ 2:1 44 C.

In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2008) 36. : Nonparametric scene parsing: label transfer via dense scene alignment. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009) 37. : Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision (ICCV), Kerkyra, pp. 1150–1157 (1999) 38. : An iterative image registration technique with an application to stereo vision. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp.

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