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Titlebook: Computer Vision in the Infrared Spectrum; Challenges and Appro Michael Teutsch,Angel D. Sappa,Riad I. Hammoud Book 2022 Springer Nature Swi

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发表于 2025-3-21 19:37:16 | 显示全部楼层 |阅读模式
书目名称Computer Vision in the Infrared Spectrum
副标题Challenges and Appro
编辑Michael Teutsch,Angel D. Sappa,Riad I. Hammoud
视频video
丛书名称Synthesis Lectures on Computer Vision
图书封面Titlebook: Computer Vision in the Infrared Spectrum; Challenges and Appro Michael Teutsch,Angel D. Sappa,Riad I. Hammoud Book 2022 Springer Nature Swi
描述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 tas
出版日期Book 2022
版次1
doihttps://doi.org/10.1007/978-3-031-01826-8
isbn_softcover978-3-031-00698-2
isbn_ebook978-3-031-01826-8Series ISSN 2153-1056 Series E-ISSN 2153-1064
issn_series 2153-1056
copyrightSpringer Nature Switzerland AG 2022
The information of publication is updating

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Detection, Classification, and Tracking, nowadays deep learning techniques are utilized to model prior knowledge about object or scene appearance using labeled training samples [203, 204]. After a . phase, these models are then applied in real world applications, which is called ..
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Summary and Outlook,sor suites often come with a combination of cameras and other sensors that can be smartly combined to complement each other. In contrast to multi-spectral computer vision, where information is fused and processed together, here we aimed at describing approaches, where one spectral band guides or aid
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Silke Konsorski-Lang,Michael Hampentent analysis like object detection [25]. Especially for real-time video processing at low latency it can be better to handle image perturbation implicitly in order to minimize the processing time of an algorithm. This can be achieved by making algorithms for image content analysis robust or even i
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