Statistical image processing and multidimensional modeling
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The work Statistical image processing and multidimensional modeling represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
The Resource
Statistical image processing and multidimensional modeling
Resource Information
The work Statistical image processing and multidimensional modeling represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Statistical image processing and multidimensional modeling
 Statement of responsibility
 Paul Fieguth
 Subject

 Electronic books
 Image processing  Statistical methods
 Image processing  Statistical methods
 Beeldverwerking
 Image processing  Statistical methods
 Markovprocessen
 Image processing  Statistical methods
 Computer graphics
 Civil engineering
 Electrical engineering
 Engineering
 Multidimensionale schaalmethoden
 Electronic resources
 Kalmanfilters
 Image processing  Statistical methods
 Statistische methoden
 Informatique
 TECHNOLOGY & ENGINEERING  Imaging Systems
 Random walks (statistiek)
 Image processing  Statistical methods
 Language
 eng
 Summary
 Images are all around us! The proliferation of lowcost, highquality 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 imageprocessing 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, climatechange 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
 Cataloging source
 GW5XE
 Image bit depth
 0
 LC call number
 TA1637
 LC item number
 .F54 2011
 Literary form
 non fiction
 Nature of contents
 dictionaries
 Series statement
 Information Science and Statistics,
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