000			03888nam a22005415i 4500
001			978-1-84628-688-9
003			DE-He213
005			20170628033903.0
007			cr nn 008mamaa
008			100301s2007 xxk| s |||| 0|eng d
020			_a9781846286889 _9978-1-84628-688-9
024	7		_a10.1007/978-1-84628-688-9 _2doi
050		4	_aT385
050		4	_aTA1637-1638
050		4	_aTK7882.P3
072		7	_aUYQV _2bicssc
072		7	_aCOM016000 _2bisacsh
082	0	4	_a006.6 _223
100	1		_aFavaro, Paolo. _eauthor.
245	1	0	_a3-D Shape Estimation and Image Restoration _h[electronic resource] : _bExploiting Defocus and Motion Blur / _cby Paolo Favaro, Stefano Soatto.
264		1	_aLondon : _bSpringer London, _c2007.
300			_aXIV, 249 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aBasic models of image formation -- Some analysis: When can 3-D shape be reconstructed from blurred images? -- Least-squares shape from defocus -- Enforcing positivity: Shape from defocus and image restoration by minimizing I-divergence -- Defocus via diffusion: Modeling and reconstruction -- Dealing with motion: Unifying defocus and motion blur -- Dealing with multiple moving objects -- Dealing with occlusions -- Final remarks.
520			_aImages contain information about the spatial properties of the scene they depict. When coupled with suitable assumptions, images can be used to infer three-dimensional information. This useful volume concentrates on motion blur and defocus, which can be exploited to infer the 3-D structure of a scene—as well as its radiance properties—and which in turn can be used to generate novel images with better quality. 3-D Shape Estimation and Image Restoration presents a coherent framework for the analysis and design of algorithms to estimate 3-D shape from defocused and motion blurred images, and to eliminate defocus and motion blur to yield "restored" images. It provides a collection of algorithms that are optimal with respect to the chosen model and estimation criterion. Topics and Features include: • Comprehensive introduction to guide readers through the different areas of the topic • Basic models of image formation • Discussion of least-squares shape from defocus • Unifying defocus and motion blur • Handling multiple moving objects • Dealing with occlusions • Appendices supply the necessary background in optimization and regularization • www.eps.hw.ac.uk/~pf21/FavaroSoattoBook/downloads contains implementations of relevant algorithms, test data and demos. Written for readers with interests in image processing and computer vision and with backgrounds in engineering, science or mathematics, this highly practical text/reference is accessible to advanced students or those with a degree that includes basic linear algebra and calculus courses. It can also be seen as a resource for practitioners looking to expand their knowledge in the subject.
650		0	_aComputer science.
650		0	_aComputer vision.
650		0	_aComputer graphics.
650		0	_aAlgorithms.
650		0	_aPhysical optics.
650	1	4	_aComputer Science.
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
650	2	4	_aAlgorithms.
650	2	4	_aImage Processing and Computer Vision.
650	2	4	_aSignal, Image and Speech Processing.
650	2	4	_aComputer Graphics.
650	2	4	_aApplied Optics, Optoelectronics, Optical Devices.
700	1		_aSoatto, Stefano. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781846281761
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-84628-688-9
912			_aZDB-2-SCS
999			_c17306 _d17306