并行压缩成像系统的压缩域小目标检测

王敏敏; 孙胜利

doi:10.3788/OPE.20162410.2549

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并行压缩成像系统的压缩域小目标检测

信息科学 | 更新时间：2020-07-07

- 并行压缩成像系统的压缩域小目标检测
- Small target detection in compressed domain for parallel compressive imaging system
- 光学精密工程 2016年24卷第10期页码：2549-2556
- 作者机构：
  
  1.中国科学院上海技术物理研究所中国科学院红外探测与成像技术重点实验室, 上海 200083
  2.中国科学院大学, 北京 100049
- 作者简介：
  
  [ "王敏敏(1990-),女,浙江嘉兴人,博士研究生,2013年于山东大学获得学士学位,主要从事压缩感知成像、目标检测与跟踪方面的研究。E-mail:wangminmindata@163.com" ]
  孙胜利(1970-),男,内蒙古通辽人,博士,研究员,博士生导师,1996年于哈尔滨工业大学获得硕士学位,1999年于中国科学院上海技术物理研究所获得博士学位,主要从事光电仪器的现代设计方法、数字化制造和全过程定量化测试等研究。E-mail:palm_sun@163.com
- 基金信息：
  
  国家863高技术研究发展计划资助项目(2015AA7015091);中国科学院上海技术物理研究所2015年所创新专项资助项目(CX-63)
- DOI：10.3788/OPE.20162410.2549
  中图分类号： TP391.4
- 收稿日期：2016-07-12，
  
  录用日期：2016-8-12，
  
  纸质出版日期：2016-10
- 稿件说明：
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王敏敏, 孙胜利. 并行压缩成像系统的压缩域小目标检测[J]. 光学精密工程, 2016,24(10):2549-2556.

Min-min WANG, Sheng-li SUN. Small target detection in compressed domain for parallel compressive imaging system[J]. Optics and precision engineering, 2016, 24(10): 2549-2556.
王敏敏, 孙胜利. 并行压缩成像系统的压缩域小目标检测[J]. 光学精密工程, 2016,24(10):2549-2556. DOI： 10.3788/OPE.20162410.2549.

Min-min WANG, Sheng-li SUN. Small target detection in compressed domain for parallel compressive imaging system[J]. Optics and precision engineering, 2016, 24(10): 2549-2556. DOI： 10.3788/OPE.20162410.2549.

摘要

提出一种适用于并行压缩成像系统的压缩域小目标检测算法，以便省去获得小目标位置信息时进行的图像重建环节，有效降低算法的复杂度。该方法通过并行压缩成像数学模型捕获背景以及待测图像压缩测量值，通过高斯混合模型进行压缩域背景建模，从而获得压缩域前景观测值。然后计算压缩域前景观测值与各压缩域目标位置模板的余弦相似度，根据局部阈值以及压缩域候选目标面积实现目标检测与定位。最后进行了仿真实验，分析了降采样率、测量次数、投影误差以及噪声等对目标检测效果的影响。结果表明：增大降采样率及噪声均会降低检测效果；测量次数对检测效果的贡献是有限的；测量次数为2次或3次时，可以在保证检测效果的同时有效控制运行时间。此外，噪声对检测效果影响较大，因而需要严格控制系统噪声。该方法可以在不进行任何图像重建的情况下实现目标的实时检测。

Abstract

A small target detection algorithm working at a compressed domain was proposed for parallel compressive imaging systems to reduce the computational complexity by eliminating the process of image reconstruction. A mathematical model of the parallel compressive imaging system was used to capture measuring values of background and current frames. Then

the background measurements were updated according to a compressive sensing-mixture of Gaussians model (CS-MoG) to obtain the measurement values of the foreground. The cosine similarities between the measurements of current frame and the compressed target-location templates were calculated. And the local threshold and target area in the compressed domain were adopted to screen candidate targets. Finally

the effects of down-sampling rate

number of measurements

projection error and noise on the detection results were studied by simulation experiments. Experimental results show that large down-sampling rate and noise would decrease the detection performance

but the number of measurements to detection results has limited contribution. When 2 or 3 measurements are set

the operation time could be controlled while ensuring the detection performance. It suggests that the noise in the system should be controlled strictly because the noise effects the detection ability greatly. Furthermore

the proposed algorithm can achieve real-time target detection without any image reconstruction.

关键词

Keywords

references

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