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001 978-3-319-23048-1
003 DE-He213
005 20200421111207.0
007 cr nn 008mamaa
008 151121s2016 gw | s |||| 0|eng d
020 _a9783319230481
_9978-3-319-23048-1
024 7 _a10.1007/978-3-319-23048-1
_2doi
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
072 7 _aTTBM
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aCOM073000
_2bisacsh
082 0 4 _a621.382
_223
245 1 0 _aDense Image Correspondences for Computer Vision
_h[electronic resource] /
_cedited by Tal Hassner, Ce Liu.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXII, 295 p. 152 illus., 146 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction to Dense Optical Flow -- SIFT Flow: Dense Correspondence across Scenes and its Applications -- Dense, Scale-Less Descriptors -- Scale-Space SIFT Flow -- Dense Segmentation-aware Descriptors -- SIFTpack: A Compact Representation for Efficient SIFT Matching -- In Defense of Gradient-Based Alignment on Densely Sampled Sparse Features -- From Images to Depths and Back -- DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling -- Joint Inference in Image Datasets via Dense Correspondence -- Dense Correspondences and Ancient Texts.
520 _aThis 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.   �         Provides in-depth coverage of dense-correspondence estimation �         Covers both the breadth and depth of new achievements in dense correspondence estimation and their applications �         Includes information for designing computer vision systems that rely on efficient and robust correspondence estimation  .
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aImage processing.
650 0 _aElectrical engineering.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aCommunications Engineering, Networks.
700 1 _aHassner, Tal.
_eeditor.
700 1 _aLiu, Ce.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319230474
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-23048-1
912 _aZDB-2-ENG
942 _cEBK
999 _c54201
_d54201