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  • Teutsch, Michael; Sappa, Angel D.; Hammoud, Riad I.

    Idioma: Inglés

    Publicado por Springer, 2021

    ISBN 10: 3031006984 ISBN 13: 9783031006982

    Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America

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  • Teutsch, Michael; Sappa, Angel D.; Hammoud, Riad I.

    Idioma: Inglés

    Publicado por Springer, 2021

    ISBN 10: 3031006984 ISBN 13: 9783031006982

    Librería: California Books, Miami, FL, Estados Unidos de America

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  • Teutsch, Michael; Sappa, Angel D.; Hammoud, Riad I.

    Idioma: Inglés

    Publicado por Springer, 2021

    ISBN 10: 3031006984 ISBN 13: 9783031006982

    Librería: Ria Christie Collections, Uxbridge, Reino Unido

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    Condición: New. In English.

  • Teutsch, Michael

    Idioma: Inglés

    Publicado por Springer 2021-10, 2021

    ISBN 10: 3031006984 ISBN 13: 9783031006982

    Librería: Chiron Media, Wallingford, Reino Unido

    Calificación del vendedor: 4 de 5 estrellas Valoración 4 estrellas, Más información sobre las valoraciones de los vendedores

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    PF. Condición: New.

  • Michael Teutsch

    Idioma: Inglés

    Publicado por Springer Nature Switzerland, Springer International Publishing Okt 2021, 2021

    ISBN 10: 3031006984 ISBN 13: 9783031006982

    Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania

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    Taschenbuch. Condición: Neu. Neuware -Human 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 140 pp. Englisch.

  • Michael Teutsch

    Idioma: Inglés

    Publicado por Springer International Publishing, 2021

    ISBN 10: 3031006984 ISBN 13: 9783031006982

    Librería: AHA-BUCH GmbH, Einbeck, Alemania

    Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

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    Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Human 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.

  • Michael Teutsch

    Idioma: Inglés

    Publicado por Springer International Publishing Okt 2021, 2021

    ISBN 10: 3031006984 ISBN 13: 9783031006982

    Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania

    Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

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    Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Human 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. 140 pp. Englisch.

  • Teutsch, Michael|Sappa, Angel D.|Hammoud, Riad I.

    Idioma: Inglés

    Publicado por Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2021

    ISBN 10: 3031006984 ISBN 13: 9783031006982

    Librería: moluna, Greven, Alemania

    Calificación del vendedor: 4 de 5 estrellas Valoración 4 estrellas, Más información sobre las valoraciones de los vendedores

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    EUR 51,51

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    Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Human 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 s.

  • Michael Teutsch (u. a.)

    Idioma: Inglés

    Publicado por Springer, 2021

    ISBN 10: 3031006984 ISBN 13: 9783031006982

    Librería: preigu, Osnabrück, Alemania

    Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

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    EUR 53,50

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    Taschenbuch. Condición: Neu. Computer Vision in the Infrared Spectrum | Challenges and Approaches | Michael Teutsch (u. a.) | Taschenbuch | x | Englisch | 2021 | Springer | EAN 9783031006982 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.