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008			150903s2009 xxk| o |||| 0|eng d
020			_a9781848824911 _99781848824911
024	7		_a10.1007/9781848824911 _2doi
035			_avtls000344451
039		9	_a201509030407 _bVLOAD _c201405050307 _dVLOAD _y201402061257 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aT385
100	1		_aHyvärinen, Aapo. _eautor _9322188
245	1	0	_aNatural Image Statistics : _bA Probabilistic Approach to Early Computational Vision / _cby Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer.
264		1	_aLondon : _bSpringer London, _c2009.
300			_axIx, 448 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aComputational Imaging and Vision, _x1381-6446 ; _v39
500			_aSpringer eBooks
505	0		_aBackground -- Linear Filters and Frequency Analysis -- Outline of the Visual System -- Multivariate Probability and Statistics -- Statistics of Linear Features -- Principal Components and Whitening -- Sparse Coding and Simple Cells -- Independent Component Analysis -- Information-Theoretic Interpretations -- Nonlinear Features and Dependency of Linear Features -- Energy Correlation of Linear Features and Normalization -- Energy Detectors and Complex Cells -- Energy Correlations and Topographic Organization -- Dependencies of Energy Detectors: Beyond V1 -- Overcomplete and Non-negative Models -- Lateral Interactions and Feedback -- Time, Color, and Stereo -- Color and Stereo Images -- Temporal Sequences of Natural Images -- Conclusion -- Conclusion and Future Prospects -- Appendix: Supplementary Mathematical Tools -- Optimization Theory and Algorithms -- Crash Course on Linear Algebra -- The Discrete Fourier Transform -- Estimation of Non-normalized Statistical Models.
520			_aOne of the most successful frameworks in computational neuroscience is modelling visual processing using the statistical structure of natural images. In this framework, the visual system of the brain constructs a model of the statistical regularities of the incoming visual data. This enables the visual system to perform efficient probabilistic inference. The same framework is also very useful in engineering applications such as image processing and computer vision. This book is the first comprehensive introduction to the multidisciplinary field of natural image statistics and its intention is to present a general theory of early vision and image processing in a manner that can be approached by readers from a variety of scientific backgrounds. A wealth of relevant background material is presented in the first section as an introduction to the subject. Following this are five unique sections, carefully selected so as to give a clear overview of all the basic theory, as well as the most recent developments and research. This structure, together with the included exercises and computer assignments, also make it an excellent textbook. Natural Image Statistics is a timely and valuable resource for advanced students and researchers in any discipline related to vision, such as neuroscience, computer science, psychology, electrical engineering, cognitive science or statistics.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aHurri, Jarmo. _eautor _9322189
700	1		_aHoyer, Patrik O. _eautor _9322190
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781848824904
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-84882-491-1 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c291082 _d291082