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008			220601s2019 sz | s |||| 0|eng d
020			_a9783031025419
024	7		_a10.1007/978-3-031-02541-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK6680.3 _b2019 EB
100	1		_aBraun, Henry _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687599
245	1	0	_aReconstruction-Free Compressive Vision for Surveillance Applications _cby Henry Braun, Pavan Turaga, Andreas Spanias, Sameeksha Katoch, Suren Jayasuriya, Cihan Tepedelenlioglu
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XIII, 86 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Signal Processing _x1932-1694
505	0		_aPreface -- Acknowledgments -- Introduction -- Compressed Sensing Fundamentals -- Computer Vision and Image Processing for Surveillance Applications -- Toward Compressive Vision -- Conclusion -- Bibliography -- Authors' Biographies.
520			_aCompressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision and deep learning have enabled new applications of automated systems. In this book, we introduce reconstruction-free compressive vision, where image processing and computer vision algorithms are embedded directly in the compressive domain, without the need for first reconstructing the measurements into images or video. Reconstruction of CS images is computationally expensive and adds to system complexity. Therefore, reconstruction-free compressive vision is an appealing alternative particularly for power-aware systems and bandwidth-limited applications that do not have on-board post-processing computational capabilities. Engineers must balance maintaining algorithm performance while minimizing both the number of measurements needed and the computational requirements of the algorithms. Our study explores the intersection of compressed sensing and computer vision, with the focus on applications in surveillance and autonomous navigation. Other applications are also discussed at the end and a comprehensive list of references including survey papers are given for further reading.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9668436 _aVisión artificial (Robótica)
650		7	_2embne _9166861 _aVigilancia electrónica
650		7	_2embne _9441179 _aRedes de sensores inalámbricas
700			_aTuraga, Pavan K. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _997415
700	1		_aSpanias, Andreas _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686104
700	1		_aKatoch, Sameeksha _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687608
700	1		_aJayasuriya, Suren _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687642
700	1		_aTepedelenlioğlu, Cihan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686553
776	0	8	_iPrinted edition: _z9783031003349
776	0	8	_iPrinted edition: _z9783031014130
776	0	8	_iPrinted edition: _z9783031036699
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02541-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI