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008			220601s2017 sz | s |||| 0|eng d
020			_a9783031025945
024	7		_a10.1007/978-3-031-02594-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1637.5 _b2017 EB
100	1		_aPreusser, Tobias _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686410
245	1	0	_aStochastic Partial Differential Equations for Computer Vision with Uncertain Data _cby Tobias Preusser, Robert M. Kirby, Torben Pätz
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XIV, 150 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Visual Computing: Computer Graphics Animation Computational Photography and Imaging _x2469-4223
505	0		_aPreface -- Notation -- Introduction -- Partial Differential Equations and Their Numerics -- Review of PDE-Based Image Processing -- Numerics of Stochastic PDEs -- Stochastic Images -- Image Processing and Computer Vision with Stochastic Images -- Sensitivity Analysis -- Conclusions -- Bibliography -- Authors' Biographies .
520			_aIn image processing and computer vision applications such as medical or scientific image data analysis, as well as in industrial scenarios, images are used as input measurement data. It is good scientific practice that proper measurements must be equipped with error and uncertainty estimates. For many applications, not only the measured values but also their errors and uncertainties, should be-and more and more frequently are-taken into account for further processing. This error and uncertainty propagation must be done for every processing step such that the final result comes with a reliable precision estimate. The goal of this book is to introduce the reader to the recent advances from the field of uncertainty quantification and error propagation for computer vision, image processing, and image analysis that are based on partial differential equations (PDEs). It presents a concept with which error propagation and sensitivity analysis can be formulated with a set of basic operations. The approach discussed in this book has the potential for application in all areas of quantitative computer vision, image processing, and image analysis. In particular, it might help medical imaging finally become a scientific discipline that is characterized by the classical paradigms of observation, measurement, and error awareness. This book is comprised of eight chapters. After an introduction to the goals of the book (Chapter 1), we present a brief review of PDEs and their numerical treatment (Chapter 2), PDE-based image processing (Chapter 3), and the numerics of stochastic PDEs (Chapter 4). We then proceed to define the concept of stochastic images (Chapter 5), describe how to accomplish image processing and computer vision with stochastic images (Chapter 6), and demonstrate the use of these principles for accomplishing sensitivity analysis (Chapter 7). Chapter 8 concludes the book and highlights new research topics for the future.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9145456 _aEcuaciones en derivadas parciales
650		7	_2embne _9667934 _aProceso de imágenes _xModelos matemáticos
650		7	_2embne _9413188 _aProceso digital de imágenes
700	1		_aKirby, Robert M. _d1975- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686412
700	1		_aPätz, Torben _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686411
776	0	8	_iPrinted edition: _z9783031014666
776	0	8	_iPrinted edition: _z9783031037221
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02594-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _esc _zSI