000 04269nam a22004215i 4500
001 978-1-84800-279-1
003 DE-He213
005 20170628033928.0
007 cr nn 008mamaa
008 100301s2009 xxk| s |||| 0|eng d
020 _a9781848002791
_9978-1-84800-279-1
024 7 _a10.1007/978-1-84800-279-1
_2doi
100 1 _aLi, Stan Z.
_eauthor.
245 1 0 _aMarkov Random Field Modeling in Image Analysis
_h[electronic resource] /
_cby Stan Z. Li.
264 1 _aLondon :
_bSpringer London,
_c2009.
300 _bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Pattern Recognition,
_x1617-7916
505 0 _aMathematical MRF Models -- Low-Level MRF Models -- High-Level MRF Models -- Discontinuities in MRF's -- MRF Model with Robust Statistics -- MRF Parameter Estimation -- Parameter Estimation in Optimal Object Recognition -- Minimization – Local Methods -- Minimization – Global Methods.
520 _aMarkov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles. This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimization. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution. Easy-to-follow and coherent, the revised edition is accessible, includes the most recent advances, and has new and expanded sections on such topics as: Conditional Random Fields; Discriminative Random Fields; Total Variation (TV) Models; Spatio-temporal Models; MRF and Bayesian Network (Graphical Models); Belief Propagation; Graph Cuts; and Face Detection and Recognition. Features: • Focuses on applying Markov random fields to computer vision problems, such as image restoration and edge detection in the low-level domain, and object matching and recognition in the high-level domain • Introduces readers to the basic concepts, important models and various special classes of MRFs on the regular image lattice, and MRFs on relational graphs derived from images • Presents various vision models in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation • Uses a variety of examples to illustrate how to convert a specific vision problem involving uncertainties and constraints into essentially an optimization problem under the MRF setting • Studies discontinuities, an important issue in the application of MRFs to image analysis • Examines the problems of model parameter estimation and function optimization in the context of texture analysis and object recognition • Includes an extensive list of references This broad-ranging and comprehensive volume is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses relating to these areas.
650 0 _aComputer science.
650 0 _aComputer vision.
650 0 _aOptical pattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aMathematics of Computing.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aPattern Recognition.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781848002784
830 0 _aAdvances in Pattern Recognition,
_x1617-7916
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-84800-279-1
912 _aZDB-2-SCS
999 _c17499
_d17499