期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

一种结合分形维的高斯混合模型目标检测方法 被引量：2

Gaussians Mixture Model Object Detection Method with Fractal Dimension
下载PDF
导出
摘要 针对树叶飘落、树枝摇动等自然背景的变化对目标检测带来的影响,提出一种结合分形维的高斯混合模型(GMM)目标检测方法。利用差分盒子维求取图像分形维数,通过设定分形维阈值去除自然背景,采用GMM方法进行目标检测。结果证明,该方法比传统的目标检测方法具有更好的检测效果。 In this paper, a Gaussians Mixtures Model（GMM） object detection method combining fractal dimension is put forward aiming at effects caused by changes of natural background. Differential Box Counting（DBC） is used to get the image fraetal dimension. A fractal dimension threshold is set to eliminate the natural background. The GMM method is utilized to detect the object. Experimental results show that the performance of the proposed method is better than that of traditional methods.
作者 袁宝峰 吴乐华 曾伟
机构地区 重庆通信学院信号与信息处理实验室
出处 《计算机工程》 CAS CSCD 北大核心 2010年第11期180-182,共3页 Computer Engineering
基金 重庆市自然科学基金资助项目(2007BB2105)
关键词 目标检测 高斯混合模型 差分盒子维 object detection Gaussians Mixture Model（GMM） Differential Box Counting（DBC）
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献5

  • 1Anderson C, Bert P, Vander W G. Change Detection and Tracking Using Pyramids Transformation Techniques[C]//Proc. of SPIE Conference on Intelligent Robots and Computer Vision. Cambridge, MA, USA: [s. n.], 1985: 72-78.
  • 2Cutler R, Davis L. View-based Detection[C]//Proc. of International Conference on Pattern Recognition. Brisbane, Australia: [s. n.], 1998: 495-500.
  • 3Stauffer C, Grimson W. Adaptive Background Mixture Models for Real-time Tracking[C]//Proc. of IEEE Conference on Computer Vision and Pattern Recognition. Fort Collins, Colorado, USA: [s. n.], 1999: 246-252.
  • 4Mandelbrot B B. The Fractal Geometry of Nature[M]. San Francisco, USA: W H Freeman, 1982.
  • 5Fisher Y, Jacobs E W, Boss R D. Fractal Image Compression Using Iterated Transforms Image and Text Compression[M]. Norwell, MA, USA: Kluwer Academic Publishers, 1992.

同被引文献16

  • 1Zha Yufei Bi Duyan.Adaptive learning algorithm based on mixture Gaussian background[J].Journal of Systems Engineering and Electronics,2007,18(2):369-376. 被引量：9
  • 2Collins R.Mean-Shift Blob Tracking Through Scale Space[C]// Proc.of IEEE Conf.on Computer Vision and Pattern Recognition.[S.l.]: IEEE Press,2003.
  • 3Neal R M,Hinton G E.A New View of the EM Algorithm That Justices Incremental,Sparse and Other Variants[EB/OL].(1998-07-03).http://citeseerx.ist.psu.edu/viewdoc/summary?doi= 10.1.1.50.5036.
  • 4Lindeberg T.Feature Detection with Automatic Scale Selection[J].International Journal of Computer Vision,1998,30(2): 77-116.
  • 5Stauffer C,Grimson W E L.Adaptive Background Mixture Models for Real-time Tracking[C]//Proc.of Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE Press,1999:246-252.
  • 6Lee D S.Effective Gaussian Mixture Learning for Video Back-ground Subtraction[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,2005,27(5):827-832.
  • 7Shimada A,Arita D.Dynamic Control of Adaptive Mixture of Gaussians Background Model[C]//Proc.of International Conference on Advanced Video and Signal Based Surveillance.Sydney,Australia:IEEE Press,2006.
  • 8Haque M,Murshed M,Paul M.A Hybrid Object Detection Technique from Dynamic Background Using Gaussian Mixture Models[C]//Proc.of the 10th Workshop on Multimedia Signal Processing.[S.l.]:IEEE Press,2008.
  • 9梁华,刘云辉.自适应多模快速背景差算法[J].中国图象图形学报,2008,13(2):345-350. 被引量：6
  • 10吴海松,华庆一,李光俊,沈婧.体育视频中的运动员检测与跟踪[J].计算机工程,2008,34(19):230-232. 被引量：8

引证文献2

二级引证文献11

计算机工程
2010年 第11期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部