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Sparse aperture 3D passive image sensing and recognition.

机译:稀疏光圈3D被动图像感应和识别。

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摘要

The way we perceive, capture, store, communicate and visualize the world has greatly changed in the past century Novel three dimensional (3D) imaging and display systems are being pursued both in academic and industrial settings. In many cases, these systems have revolutionized traditional approaches and/or enabled new technologies in other disciplines including medical imaging and diagnostics, industrial metrology, entertainment, robotics as well as defense and security.;In this dissertation, we focus on novel aspects of sparse aperture multi-view imaging systems and their application in quantum-limited object recognition in two separate parts. In the first part, two concepts are proposed. First a solution is presented that involves a generalized framework for 3D imaging using randomly distributed sparse apertures. Second, a method is suggested to extract the profile of objects in the scene through statistical properties of the reconstructed light field. In both cases, experimental results are presented that demonstrate the feasibility of the techniques.;In the second part, the application of 3D imaging systems in sensing and recognition of objects is addressed. In particular, we focus on the scenario in which only 10s of photons reach the sensor from the object of interest, as opposed to hundreds of billions of photons in normal imaging conditions. At this level, the quantum limited behavior of light will dominate and traditional object recognition practices may fail. We suggest a likelihood based object recognition framework that incorporates the physics of sensing at quantum-limited conditions. Sensor dark noise has been modeled and taken into account. This framework is applied to 3D sensing of thermal objects using visible spectrum detectors. Thermal objects as cold as 250K are shown to provide enough signature photons to be sensed and recognized within background and dark noise with mature, visible band, image forming optics and detector arrays. The results suggest that one might not need to venture into exotic and expensive detector arrays and associated optics for sensing room-temperature thermal objects in complete darkness.
机译:在过去的一个世纪中,我们感知,捕获,存储,交流和可视化世界的方式发生了巨大变化,在学术和工业环境中都在追求新颖的三维(3D)成像和显示系统。在许多情况下,这些系统彻底改变了传统方法,并且/或者在医学成像和诊​​断,工业计量,娱乐,机器人技术以及国防和安全等其他领域启用了新技术。孔径多视图成像系统及其在量子受限物体识别中的应用分为两个部分。在第一部分中,提出了两个概念。首先提出一种解决方案,其中涉及使用随机分布的稀疏孔径进行3D成像的通用框架。其次,提出了一种通过重建光场的统计特性提取场景中物体轮廓的方法。在这两种情况下,实验结果均证明了该技术的可行性。第二部分,探讨了3D成像系统在物体感测和识别中的应用。特别是,我们关注的场景是只有十个光子从感兴趣的对象到达传感器,而不是正常成像条件下的数千亿个光子。在这个水平上,光的量子受限行为将占主导地位,传统的物体识别实践可能会失败。我们建议一种基于可能性的物体识别框架,该框架应结合量子受限条件下的传感物理学。传感器暗噪声已建模并考虑在内。该框架适用于使用可见光谱检测器对热物体进行3D感测。显示的温度低至250K的热物体可提供足够的特征性光子,以便在背景和暗噪声中利用成熟的可见光带,成像光学器件和检测器阵列来感测和识别。结果表明,人们可能无需冒险去购买昂贵且昂贵的检测器阵列和相关的光学器件,以在完全黑暗的环境中感测室温下的热物体。

著录项

  • 作者

    DaneshPanah, Mehdi.;

  • 作者单位

    University of Connecticut.;

  • 授予单位 University of Connecticut.;
  • 学科 Engineering Electronics and Electrical.;Physics Optics.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 135 p.
  • 总页数 135
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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