Dense Image Correspondences for Computer Vision [Hardcover]

This book describes the fundamental building-block of many new computer vision systems: dense and robust correspondence estimation. Dense correspondence estimation techniques are now successfully being used to solve a wide range of computer vision problems, very different from the traditional applications such techniques were originally developed to solve. This book introduces the techniques used for establishing correspondences between challenging image pairs, the novel features used to make these techniques robust, and the many problems dense correspondences are now being used to solve. The book provides information to anyone attempting to utilize dense correspondences in order to solve new or existing computer vision problems. The editors describe how to solve many computer vision problems by using dense correspondence estimation. Finally, it surveys resources, code and data, necessary for expediting the development of effective correspondence-based computer vision systems.Introduction.- Part I  Establishing dense correspondences.- Classic dense-correspondence estimation  Horn & Schunck, Lucas & Kanade, and beyond.- SIFT-Flow - Correspondences beyond same-scene image pairs.- SIFTS and Scales  Correspondences with scale differences.- Patchmatch  Fast correspondence estimation.- SIFT-Pack  Same SIFTs for a smaller price.- Segmentation-Flow.- Part II  Dense correspondences and their applications.- Depth by example  From images to depth and back.- Label-transfer.- Depth-transfer.- Image similarity.- Dense-Flow and ancient texts.- Computational photography  super-resolution and de-noising.- Biometrics.- Image hallucination.- Part III  Analyzing images as videos.- Annotation propagation.- Object discovery and co-segmentation.- Appendix.Prof. Tal Hassner is a faculty member of the Department of Mathematics andComputer Science, The Open University of Israel, Israel. Ce Liu is a Researcher with Google.

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