期刊文献+

一种视频序列中的运动对象自动分割算法 被引量:1

A New Method for Moving Object Segmentation Automatically in Video Sequence
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摘要 提出了一种自动、准确的运动对象分割算法。首先通过直方图拟合获得准确的背景噪声方差,克服了以往只能依据经验设定背景噪声方差的缺点,并使用显著性测试技术有效地对帧差图进行二值化,确定出运动区域。然后进行形态学和对称差分处理消除噪声及显露背景,获得初始运动对象。但由于分割结果不够精确,再使用梯度向量流场作为外力的改进活动轮廓算法得到运动对象精确轮廓。实验结果表明,该方法能够得到运动对象精确的轮廓,并且具有调整参数少,抗干扰能力强,可并行处理等优点。 An automatic and accurate moving object segmentation algorithm is proposed in this paper. Firstly, to overcome the shortcoming of setting the value of background noise variance by experience, we estimate the value by histogram fitting. At the same time, we use the significance test to threshold the difference image and extract the moving areas. Then, the residual noise and the uncovered background due to the motions of objects are fast and effectively eliminated by morphological operations and intersection of two symmetrical moving areas. Thus the initial moving object and its contour can be obtained. However, the segmentation result is not very accurate. To solve this problem, an improved active contour which uses the gradient vector as the external force guides the initial contour moving to the actual video object contour. Experiment results testify that the proposed algorithm not only can obtain the closed and accurate video object contour but also has the properties of few parameters, robust to noise, and parallel processing
出处 《计算机科学》 CSCD 北大核心 2007年第6期239-241,247,共4页 Computer Science
基金 国家自然科学基金(60141002) 南昌航院测控中心开放实验室基金(KG200104001)的支持。
关键词 运动对象 背景噪声方差 帧差图 活动轮廓 Moving object, Background noise variance, Difference image, Active contour
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参考文献8

  • 1刘志,杨杰,彭宁嵩.基于假设检验和区域合并的视频对象分割[J].数据采集与处理,2004,19(2):124-129. 被引量:7
  • 2Mezaris V,Strintzis M G.Video object segmentation using Bayes-based temporal tracking and trajectory-based region merging.IEEE Trans.Circuits and Systems for Video Technology,2004,14(6):782~795
  • 3Kim C,Hwang J N.Fast and Automatic Video Object Segmentation and Tracking for Content-based Application.IEEE Trans on Circuits and Systems for Video Technology,2002,12 (2):122 ~129
  • 4杨莉,张弘,李玉山.视频运动对象的自动分割[J].计算机辅助设计与图形学学报,2004,16(3):301-306. 被引量:37
  • 5Aach T,Kaup A,Mester R.Statistical model-based change detection in moving video.Signal Processing,1993,31 (2):165 ~180
  • 6Kass M,Witkin A,Terzopoulos D.Snakes:Active contour models.International Journal Computer Vision,1988,1:321~331
  • 7Xu C Y,Princ J L.Snake,shapes and gradient vector flow.IEEE Tran.Image Processing,1998,7(3):359~369
  • 8蒋晓悦,赵荣椿.B—样条子波在图像边缘检测中的应用[J].中国体视学与图像分析,2002,7(4):198-201. 被引量:8

二级参考文献28

  • 1王春波,张卫东,张文渊,许晓鸣.复杂交通环境中车辆的视觉检测[J].上海交通大学学报,2000,34(12):1680-1682. 被引量:17
  • 2赵松年 熊小芸.子波变换与子波分析[M].北京:电子工业出版社,1997..
  • 3MPEG Video Group. Overview of the MPEG-4standard[S]. ISO/IEC JTC1/SC29/WG11 N4668. 2002.
  • 4Zhang D S, Lu G J. Segmentation of moving objects in image sequences: a review[J]. Circuits, Systems, and Signal Processing, 2001,20(2): 143~183.
  • 5Mech R, Wollborn M. A noise robust method for 2D shape estimation of moving objects in video sequences considering a moving camera[J]. Signal Processing, 1998, 66(2): 203~217.
  • 6Aach T, Kaup A, Mester R. Statistical model-based change detection in moving video[J]. Signal Processing, 1993, 31(2): 165~180.
  • 7Ziliani F, Cavallaro A. Image analysis for video sur-veillance based on spatial regularization of a statistical model-based change detection[J]. Real-Time Imaging, 2001, 7(5): 389~399.
  • 8Horn B K P, Schunck B G. Determining optical flow[J]. Artificial Intelligence, 1981, 17: 185~203.
  • 9Bierling M. Displacement estimation by hierarchical block matching[A]. Proc SPIE Visual Communications and Image Processing, VCIP[C]. USA: Cambridge, MA, 1988,1001: 942~951.
  • 10Ma K K, Wang H Y. Region-based nonparametric optical flow segmentation with pre-clustering and post-clustering[A]. IEEE International Conference on Multimedia and Expo,ICME[C]. Switzerland: Lausanne, 2002, 2: 201~204.

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