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001 978-1-4419-7294-1
003 DE-He213
005 20140220083723.0
007 cr nn 008mamaa
008 101029s2011 xxu| s |||| 0|eng d
020 _a9781441972941
_9978-1-4419-7294-1
024 7 _a10.1007/978-1-4419-7294-1
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aPD
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aFieguth, Paul.
_eauthor.
245 1 0 _aStatistical Image Processing and Multidimensional Modeling
_h[electronic resource] /
_cby Paul Fieguth.
264 1 _aNew York, NY :
_bSpringer New York,
_c2011.
300 _aXXII, 454 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInformation Science and Statistics,
_x1613-9011
505 0 _aIntroduction -- Inverse problems -- Static estimation and sampling -- Dynamic estimation and sampling -- multidimensional modelling -- Markov random fields -- Hidden markov models -- Changes of basis -- Linear systems estimation -- Kalman filtering and domain decomposition -- Sampling and monte carlo methods.
520 _aImages are all around us! The proliferation of low-cost, high-quality imaging devices has led to an explosion in acquired images. When these images are acquired from a microscope, telescope, satellite, or medical imaging device, there is a statistical image processing task: the inference of something—an artery, a road, a DNA marker, an oil spill—from imagery, possibly noisy, blurry, or incomplete. A great many textbooks have been written on image processing. However this book does not so much focus on images, per se, but rather on spatial data sets, with one or more measurements taken over a two or higher dimensional space, and to which standard image-processing algorithms may not apply. There are many important data analysis methods developed in this text for such statistical image problems. Examples abound throughout remote sensing (satellite data mapping, data assimilation, climate-change studies, land use), medical imaging (organ segmentation, anomaly detection), computer vision (image classification, segmentation), and other 2D/3D problems (biological imaging, porous media). The goal, then, of this text is to address methods for solving multidimensional statistical problems. The text strikes a balance between mathematics and theory on the one hand, versus applications and algorithms on the other, by deliberately developing the basic theory (Part I), the mathematical modeling (Part II), and the algorithmic and numerical methods (Part III) of solving a given problem. The particular emphases of the book include inverse problems, multidimensional modeling, random fields, and hierarchical methods. Paul Fieguth is a professor in Systems Design Engineering at the University of Waterloo in Ontario, Canada. He has longstanding research interests in statistical signal and image processing, hierarchical algorithms, data fusion, and the interdisciplinary applications of such methods, particularly to problems in medical imaging, remote sensing, and scientific imaging.
650 0 _aStatistics.
650 0 _aComputer science.
650 0 _aComputer vision.
650 0 _aDistribution (Probability theory).
650 1 4 _aStatistics.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aSignal, Image and Speech Processing.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441972934
830 0 _aInformation Science and Statistics,
_x1613-9011
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-7294-1
912 _aZDB-2-SCS
999 _c105731
_d105731