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基于统计量的声呐图像目标检测算法 被引量:3

Algorithm of target detection in sonar imagery based on statistical value
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摘要 水下声呐图像目标检测问题是一项重要而困难的工作,采用滑动窗计算图像中各像素点处邻域像素灰度的统计量,利用最大熵图像分割算法确定检测阈值,并利用均值、标准差、偏态和峰度等统计量对算法进行了仿真验证,对声呐图像中的目标回波区和阴影区域均可实现较好的检测效果。结果表明,该方法具有原理简单、运算效率高、实时性好等特点,具有较强的工程应用价值。 The target detection of underwater sonar imagery is a crucial task. The statistical value of pixels grey-level based on sliding-windows was calculated, certain entropy criterion was used to gain the detecting threshold. Mean,standard deviation, skewness and kurosis were respectively adopted to validate the performance of arithmetic. The target highlights region and shadow region of sonar image can be detected precisely. Simulation results indicate that this method presented in this paper has characteristics of simple principle, high efficiency and real time, then it has certain value for engineering application.
作者 田晓东 刘忠
机构地区 海军工程大学
出处 《舰船科学技术》 北大核心 2007年第1期119-122,共4页 Ship Science and Technology
关键词 统计量 目标检测 声呐图像 statistical value target detection sonar image
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参考文献8

  • 1CHANUSSOT J,MAUSSSANG F.Scalar Image filters for speckle reduction on synthetic aperture sonar images[J].IEEE TRANSACTION ON IMAGE PROCESSING,2002,(1):1234-1241.
  • 2MIGNOTTE M,COLLET C,PEREZ P.Sonar image segmentation using an unsupervised hierarchical MRF model[J].IEEE TRANSACTION ON IMAGE PROCESSING,2000,9(7):1216-1231.
  • 3MIGNOTTE M,COLLET C.Three-class Markovian segmentation of high-resolution sonar image[J].Computer vision and Image Understanding,1999,76(3):191-204.
  • 4REED S,PETILLOT Y,BELL J.An automatic approach to the detection and extraction of mine features in sidescan sonar[J].IEEE JOURNAL of OCEANIC ENGINEERING,2003,28(1):90-105.
  • 5MAUSSANG F,CHANUSSOT J,HETET A.Automated Segmentation of SAS images using the Mean-Standard deviation plane for the detection of underwater mines[C].Proceedings of the Seventh European conference on underwater acoustics:ECUA 2004,Delft:The Netherlands,2004.
  • 6MAUSSANG F,CHANUSSOT J,HETET A.Higher order statistics for the detection of underwater mines in SAS imagery[C].Proceedings of the Seventh European conference on underwater acoustics:ECUA 2004.Delft:The Netherlands,2004.
  • 7景晓军,李剑峰,刘郁林.一种基于三维最大类间方差的图像分割算法[J].电子学报,2003,31(9):1281-1285. 被引量:73
  • 8李刚,韩建国.PCB图像检测中阈值化分割的研究[J].北京化工大学学报(自然科学版),2002,29(4):72-74. 被引量:7

二级参考文献22

  • 1吴一全,朱兆达.图像处理中阈值选取方法30年(1962—1992)的进展(二)[J].数据采集与处理,1993,8(4):268-282. 被引量:96
  • 2章毓晋.图像处理和分析[M].清华大学出版社,1999,3..
  • 3Pavlids T. Why progress in machine vision is so slow [ J ]. Pattern Recognition Letters, 1991,13(4) :221 - 225.
  • 4Sahoo P K,Soltani S, Wang A K C.A survey of thresholding techniques[J].Computer Vision, Graphics and Image Processing, 1988,41 (2) :233 - 260.
  • 5Pong T C, Shapiro L G, Watson L T. Experiments in segmentation using face model region grower [J]. Computer Vision, Graphics and Image Processing, 1984,25(1) :1-23.
  • 6Monga O. An optimal region growing algorithm for image segmentation[J]. Inte.J Pattern Recog. Artif. Intell, 1987,1(4) :351 - 375.
  • 7Giordana .N, Pieczynski W. Estimation of generalized multisensor hidden markov chains and unsupervised image segmentation [ J]. IEEE Trans on PAMI, 1997,19(5) :465 - 475.
  • 8Tabb M, Ahuja M. Multiscale image segmentation by integrated edge and region detection [J] .IEEE. Trans on IP, 1997,6(5) :642 -654.
  • 9Wong A K C, Sahoo P K. A gray-level threshold selection method based on maximum entropy principle [J]. IEEE. Trans.on SMC, 1989,19(4) :866 - 871.
  • 10Chanda B, Majumder D, Dutta R. A note on the use of gray-level co-occurrence matrix in threshold selection [ J]. Signal Processing, 1988,15 :149- 167.

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