000 03407nam a22005055i 4500
001 978-3-642-15352-5
003 DE-He213
005 20140220083747.0
007 cr nn 008mamaa
008 101029s2011 gw | s |||| 0|eng d
020 _a9783642153525
_9978-3-642-15352-5
024 7 _a10.1007/978-3-642-15352-5
_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
100 1 _aMitiche, Amar.
_eauthor.
245 1 0 _aVariational and Level Set Methods in Image Segmentation
_h[electronic resource] /
_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 _uhttp://dx.doi.org/10.1007/978-3-642-15352-5
912 _aZDB-2-ENG
999 _c107047
_d107047