| 書目名稱 | Computer Vision in the Infrared Spectrum |
| 副標題 | Challenges and Appro |
| 編輯 | Michael Teutsch,Angel D. Sappa,Riad I. Hammoud |
| 視頻video | http://file.papertrans.cn/235/234092/234092.mp4 |
| 叢書名稱 | Synthesis Lectures on Computer Vision |
| 圖書封面 |  |
| 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 |
| doi | https://doi.org/10.1007/978-3-031-01826-8 |
| isbn_softcover | 978-3-031-00698-2 |
| isbn_ebook | 978-3-031-01826-8Series ISSN 2153-1056 Series E-ISSN 2153-1064 |
| issn_series | 2153-1056 |
| copyright | Springer Nature Switzerland AG 2022 |
|Archiver|手機版|小黑屋|
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