长波红外离轴三反光学系统设计
[电光与控制] &&
于晓辉;&包睿;&乔鑫;&梁冰 &
长波红外 & 离轴三反光学系统 & 像差理论 & 遥感系统 & long-wave infrared & three-mirror off-axis optical system & aberration theory & remote sensing system &
摘 要 | 介绍了一种基于像差理论的离轴三反光学系统初始结构的求解方法，使用此方法设计了一个适用于空间遥感系统的离轴三反光学系统。该系统的工作波段为7.7~10.3 μm，焦距为1000 mm，系统F数为2，视场角为1°，给出了其具体的优化过程并进行了性能分析。设计结果表明,该系统满足空间遥感光学系统要求的长焦距、大口径、成像良好、结构紧凑等特点，验证了该方法的正确性和可行性。
Abstract | A method for calculating the initial structure of off-axis optical system with three mirrors based on aberration theory is introduced, and a three-mirror off-axis optical system is designed with this method,
which is adaptable for space remote sensing system.The system has a wave band of7.7~10.3 μm, effective mirror length of 1000 mm, F-number of 2, and the full FOV is 1°.The detailed optimization process is presented.It shows that the system has the advantages of long focal length, large diameter, high image quality and compact structure, which can meet the requirements of the space remote sensing system.要扩写成100字-家长要写给读书感言-急~陪孩子一起看书,我感到很快乐!在读书的过程中,我和孩子一起入情入境感受人物的喜和悲、哀和乐.学习人物的精神品质.我希望能和孩子一起读书,一起受到心灵的成长!求扩写 以家长的观念哦~我只有5财富别贪心的
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扫描下载二维码Learning online structural appearance model for robust object tracking基于在线学习结构化表观模型的视觉目标跟踪方法
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Learning online structural appearance model for robust object trackingMin YangMingTao PeiYuWei WuYunDe JiaResearch PaperDOI:
10.-014-5177-6Cite this article as: Yang, M., Pei, M., Wu, Y. et al. Sci. China Inf. Sci. (. doi:10.-014-5177-6
The main challenge of robust object tracking comes from the difficulty in designing an adaptive appearance model that is able to accommodate appearance variations. Existing tracking algorithms often perform self-updating of the appearance model with examples from recent tracking results to account for appearance changes. However, slight inaccuracy of tracking results can degrade the appearance model. In this paper, we propose a robust tracking method by evaluating an online structural appearance model based on local sparse coding and online metric learning. Our appearance model employs pooling of structural features over the local sparse codes of an object region to obtain a middle-level object representation. Tracking is then formulated by seeking for the most similar candidate within a Bayesian inference framework where the distance metric for similarity measurement is learned in an online manner to match the varying object appearance. Both qualitative and quantitative evaluations on various challenging image sequences demonstrate that the proposed algorithm outperforms the state-of-the-art methods.object trackingstructural appearance modelsparse representationonline metric learning目标跟踪结构化表观模型稀疏表示在线度量学习1.Adam A, Rivlin E, Shimshoni I. Robust fragments-based tracking using the integral histogram. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, –8052.Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell, 4–5773.Kwon J, Lee K M. Visual tracking decomposition. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, –12764.Wang H Z, Suter D, Schindler K, et al. Adaptive object tracking based on an effective appearance filter. IEEE Trans Pattern Anal Mach Intel, 61–16675.Grabner H, Grabner M, Bischof H. Real-time tracking via on-line boosting. 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IEEE Trans Pattern Anal Mach Intel, 4–586Min Yang1MingTao Pei1YuWei Wu1YunDe Jia11.Beijing Laboratory of Intelligent Information Technology, School of Computer ScienceBeijing Institute of TechnologyBeijingChina
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