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008			230425s2022 sz | s |||| 0|eng d
020			_a9783031018268
024	7		_a10.1007/978-3-031-01826-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2022 EB
100	1		_aTeutsch, Michael _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688250
245	1	0	_aComputer Vision in the Infrared Spectrum : _bChallenges and Approaches _cby Michael Teutsch, Angel D. Sappa, Riad I. Hammoud
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (X, 128 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		_aIntroduction -- Cross-Spectral Image Processing -- Detection, Classification, and Tracking -- Applications -- Summary and Outlook -- Bibliography -- Authors' Biographies.
520			_aHuman visual perception is limited to the visual-optical spectrum. Machine vision is not. Cameras sensitive to the different infrared spectra can enhance the abilities of autonomous systems and visually perceive the environment in a holistic way. Relevant scene content can be made visible especially in situations, where sensors of other modalities face issues like a visual-optical camera that needs a source of illumination. As a consequence, not only human mistakes can be avoided by increasing the level of automation, but also machine-induced errors can be reduced that, for example, could make a self-driving car crash into a pedestrian under difficult illumination conditions. Furthermore, multi-spectral sensor systems with infrared imagery as one modality are a rich source of information and can provably increase the robustness of many autonomous systems. Applications that can benefit from utilizing infrared imagery range from robotics to automotive and from biometrics to surveillance. In this book, we provide a brief yet concise introduction to the current state-of-the-art of computer vision and machine learning in the infrared spectrum. Based on various popular computer vision tasks such as image enhancement, object detection, or object tracking, we first motivate each task starting from established literature in the visual-optical spectrum. Then, we discuss the differences between processing images and videos in the visual-optical spectrum and the various infrared spectra. An overview of the current literature is provided together with an outlook for each task. Furthermore, available and annotated public datasets and common evaluation methods and metrics are presented. In a separate chapter, popular applications that can greatly benefit from the use of infrared imagery as a data source are presented and discussed. Among them are automatic target recognition, video surveillance, or biometrics including face recognition. Finally, we conclude with recommendations for well-fitting sensor setups and data processing algorithms for certain computer vision tasks. We address this book to prospective researchers and engineers new to the field but also to anyone who wants to get introduced to the challenges and the approaches of computer vision using infrared images or videos. Readers will be able to start their work directly after reading the book supported by a highly comprehensive backlog of recent and relevant literature as well as related infrared datasets including existing evaluation frameworks. Together with consistently decreasing costs for infrared cameras, new fields of application appear and make computer vision in the infrared spectrum a great opportunity to face nowadays scientific and engineering challenges.
988			_aSynthesis Collection of Technology_2022
650		7	_2embne _9159793 _aVisión por ordenador
650		7	_2embne _9145279 _aEspectroscopia infrarroja
700	1		_aSappa, Angel D. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688251
700	1		_aHammoud, Riad I. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688252
776	0	8	_iPrinted edition: _z9783031000836
776	0	8	_iPrinted edition: _z9783031006982
776	0	8	_iPrinted edition: _z9783031029547
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01826-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI