000			03963nam a22005775i 4500
001			978-3-319-23048-1
003			DE-He213
005			20220801221631.0
007			cr nn 008mamaa
008			151121s2016 sz | 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
072		7	_aTJF _2bicssc
072		7	_aUYS _2bicssc
072		7	_aTEC008000 _2bisacsh
072		7	_aTJF _2thema
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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	_aSignal processing. _94052
650		0	_aComputer vision. _957141
650		0	_aArtificial intelligence. _93407
650		0	_aTelecommunication. _910437
650	1	4	_aSignal, Speech and Image Processing . _931566
650	2	4	_aComputer Vision. _957142
650	2	4	_aArtificial Intelligence. _93407
650	2	4	_aCommunications Engineering, Networks. _931570
700	1		_aHassner, Tal. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _957143
700	1		_aLiu, Ce. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _957144
710	2		_aSpringerLink (Online service) _957145
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783319230474
776	0	8	_iPrinted edition: _z9783319230498
776	0	8	_iPrinted edition: _z9783319359144
856	4	0	_uhttps://doi.org/10.1007/978-3-319-23048-1
912			_aZDB-2-ENG
912			_aZDB-2-SXE
942			_cEBK
999			_c79881 _d79881