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020 _a9783031018268
_9978-3-031-01826-8
024 7 _a10.1007/978-3-031-01826-8
_2doi
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYT
_2thema
082 0 4 _a006
_223
100 1 _aTeutsch, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983179
245 1 0 _aComputer Vision in the Infrared Spectrum
_h[electronic resource] :
_bChallenges and Approaches /
_cby Michael Teutsch, Angel D. Sappa, Riad I. Hammoud.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aX, 128 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_983180
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputer Vision.
_983182
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aSappa, Angel D.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983184
700 1 _aHammoud, Riad I.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983185
710 2 _aSpringerLink (Online service)
_983187
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000836
776 0 8 _iPrinted edition:
_z9783031006982
776 0 8 _iPrinted edition:
_z9783031029547
830 0 _aSynthesis Lectures on Computer Vision,
_x2153-1064
_983189
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01826-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c85464
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