• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Computer Vision from Spatial-Multiplexing Cameras at Low Measurement Rates
【24h】

Computer Vision from Spatial-Multiplexing Cameras at Low Measurement Rates

机译：低测量速率下空间复用相机的计算机视觉

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In UAVs and parking lots, it is typical to first collect an enormous number of pixels using conventional imagers. This is followed by employment of expensive methods to compress by throwing away redundant data. Subsequently, the compressed data is transmitted to a ground station. The past decade has seen the emergence of novel imagers called spatial-multiplexing cameras, which offer compression at the sensing level itself by providing an arbitrary linear measurements of the scene instead of pixel-based sampling. In this dissertation, I discuss various approaches for effective information extraction from spatial-multiplexing measurements and present the trade-offs between reliability of the performance and computational/storage load of the system. In the first part, I present a reconstruction-free approach to high-level inference in computer vision, wherein I consider the specific case of activity analysis, and show that using correlation filters, one can perform effective action recognition and localization directly from a class of spatial-multiplexing cameras, called compressive cameras, even at very low measurement rates of 1%. In the second part, I outline a deep learning based non-iterative and real-time algorithm to reconstruct images from compressively sensed (CS) measurements, which can outperform the traditional iterative CS reconstruction algorithms in terms of reconstruction quality and time complexity, especially at low measurement rates. To overcome the limitations of compressive cameras, which are operated with random measurements and not particularly tuned to any task, in the third part of the dissertation, I propose a method to design spatial-multiplexing measurements, which are tuned to facilitate the easy extraction of features that are useful in computer vision tasks like object tracking. The work presented in the dissertation provides sufficient evidence to high-level inference in computer vision at extremely low measurement rates, and hence allows us to think about the possibility of revamping the current day computer systems.
机译：在无人机和停车场中，通常首先使用常规成像仪收集大量像素。其次是采用昂贵的方法通过丢弃冗余数据进行压缩。随后，将压缩数据发送到地面站。在过去的十年中，出现了称为空间多路复用相机的新型成像仪，该成像仪通过提供场景的任意线性测量而不是基于像素的采样来提供感测级别本身的压缩。在本文中，我讨论了从空间复用测量中有效提取信息的各种方法，并提出了性能可靠性与系统计算/存储负载之间的权衡。在第一部分中，我提出了一种无需重构的方法来进行计算机视觉的高级推理，其中考虑了活动分析的特定情况，并展示了使用相关过滤器可以直接从一个类中执行有效的动作识别和定位即使在非常低的1％的测量速率下，也可以使用称为“压缩摄像机”的空间多路复用摄像机。在第二部分中，我概述了一种基于深度学习的非迭代和实时算法，该算法可从压缩感知（CS）测量中重建图像，在重建质量和时间复杂度方面，该方法可以优于传统的迭代CS重建算法，尤其是在测量率低。为了克服压缩摄像机的局限性，该摄像机具有随机测量功能，并且没有专门针对任何任务进行调整，因此在论文的第三部分中，我提出了一种设计空间多路复用测量方法的方法，对这些方法进行了调整，以利于轻松提取图像。在计算机视觉任务中有用的功能，例如对象跟踪。本文的工作为以极低的测量速率对计算机视觉进行高层推理提供了充分的证据，因此使我们可以考虑改造当今计算机系统的可能性。

著录项

  • 作者

    Kulkarni, Kuldeep Sharad.;

  • 作者单位

    Arizona State University.;

  • 授予单位 Arizona State University.;
  • 学科 Electrical engineering.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 106 p.
  • 总页数 106
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号