000			03337nam a22004695i 4500
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005			20160812084303.0
007			cr nn 008mamaa
008			101029s2011 gw | s |||| 0|eng d
020			_a9783642153525 _9978-3-642-15352-5
040			_cMX-MeUAM
050		4	_aTK5102.9
050		4	_aTA1637-1638
050		4	_aTK7882.S65
082	0	4	_a621.382 _223
100	1		_aMitiche, Amar. _eauthor.
245	1	0	_aVariational and Level Set Methods in Image Segmentation _h[recurso electrónico] / _cby Amar Mitiche, Ismail Ben Ayed.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aVIII, 192 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Topics in Signal Processing, _x1866-2609 ; _v5
505	0		_aIntroduction -- Image Segmentation -- Image Models -- Optical Flow Estimation -- Joint Optical Flow Estimation and Segmentation -- Optical Flow 3D segmentation -- Appendix.
520			_aImage segmentation consists of dividing an image domain into disjoint regions according to a characterization of the image within or in-between the regions. Therefore, segmenting an image is to divide its domain into relevant components. The efficient solution of the key problems in image segmentation promises to enable a rich array of useful applications. The current major application areas include robotics, medical image analysis, remote sensing, scene understanding, and image database retrieval. The subject of this book is image segmentation by variational methods with a focus on formulations which use closed regular plane curves to define the segmentation regions and on a level set implementation of the corresponding active curve evolution algorithms. Each method is developed from an objective functional which embeds constraints on both the image domain partition of the segmentation and the image data within or in-between the partition regions. The necessary conditions to optimize the objective functional are then derived and solved numerically. The book covers, within the active curve and level set formalism, the basic two-region segmentation methods, multiregion extensions, region merging, image modeling, and motion based segmentation. To treat various important classes of images, modeling investigates several parametric distributions such as the Gaussian, Gamma, Weibull, and Wishart. It also investigates non-parametric models. In motion segmentation, both optical flow and the movement of real three-dimensional objects are studied.
650		0	_aEngineering.
650		0	_aComputer vision.
650	1	4	_aEngineering.
650	2	4	_aSignal, Image and Speech Processing.
650	2	4	_aImage Processing and Computer Vision.
700	1		_aBen Ayed, Ismail. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642153518
830		0	_aSpringer Topics in Signal Processing, _x1866-2609 ; _v5
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-15352-5
596			_a19
942			_cLIBRO_ELEC
999			_c202887 _d202887