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999			_c386831 _d386831
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007			cr nn 008mamaa
008			220601s2018 sz | s |||| 0|eng d
020			_a9783031018220
024	7		_a10.1007/978-3-031-01822-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2018 EB
100	1		_aFelsberg, Michael _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686162
245	1	0	_aProbabilistic and Biologically Inspired Feature Representations _cby Michael Felsberg
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XIII, 89 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Computer Vision _x2153-1064
505	0		_aPreface -- Acknowledgments -- Introduction -- Basics of Feature Design -- Channel Coding of Features -- Channel-Coded Feature Maps -- CCFM Decoding and Visualization -- Probabilistic Interpretation of Channel Representations -- Conclusions -- Bibliography -- Author's Biography -- Index.
520			_aUnder the title "Probabilistic and Biologically Inspired Feature Representations," this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first property is shared with many histogram- and signature-based descriptors, the latter property with the related concept of population codes. In their unique combination of properties, channel representations become a visual Swiss army knife-they can be used for image enhancement, visual object tracking, as 2D and 3D descriptors, and for pose estimation. In the chapters of this text, the framework of channel representations will be introduced and its attributes will be elaborated, as well as further insight into its probabilistic modeling and algorithmic implementation will be given. Channel representations are a useful toolbox to represent visual information for machine learning, as they establish a generic way to compute popular descriptors such as HOG, SIFT, and SHOT. Even in an age of deep learning, they provide a good compromise between hand-designed descriptors and a-priori structureless feature spaces as seen in the layers of deep networks.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9152614 _aReconocimiento de formas
650		7	_2embne _9159793 _aVisión por ordenador
650		7	_2embne _9166090 _aAprendizaje automático
776	0	8	_iPrinted edition: _z9783031000799
776	0	8	_iPrinted edition: _z9783031006944
776	0	8	_iPrinted edition: _z9783031029509
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01822-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _esc _zSI