留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

CV-GAC模型与图割优化的运动目标检测和分割

宋琳 高满屯 王三民 王淑侠

宋琳, 高满屯, 王三民, 王淑侠. CV-GAC模型与图割优化的运动目标检测和分割[J]. 机械科学与技术, 2017, 36(1): 102-107. doi: 10.13433/j.cnki.1003-8728.2017.0115
引用本文: 宋琳, 高满屯, 王三民, 王淑侠. CV-GAC模型与图割优化的运动目标检测和分割[J]. 机械科学与技术, 2017, 36(1): 102-107. doi: 10.13433/j.cnki.1003-8728.2017.0115
Song Lin, Gao Mantun, Wang Sanmin, Wang Shuxia. Detecting and Segmenting Moving Object Using CV-GAC Model with Graph Cut Optimization[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(1): 102-107. doi: 10.13433/j.cnki.1003-8728.2017.0115
Citation: Song Lin, Gao Mantun, Wang Sanmin, Wang Shuxia. Detecting and Segmenting Moving Object Using CV-GAC Model with Graph Cut Optimization[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(1): 102-107. doi: 10.13433/j.cnki.1003-8728.2017.0115

CV-GAC模型与图割优化的运动目标检测和分割

doi: 10.13433/j.cnki.1003-8728.2017.0115
基金项目: 

国家自然科学基金项目(51105310)资助

详细信息
    作者简介:

    宋琳(1975-),讲师,研究方向为计算机视觉和模式识别,songlin03@sina.com

Detecting and Segmenting Moving Object Using CV-GAC Model with Graph Cut Optimization

  • 摘要: 针对视频图像序列中运动目标的分割问题,提出了一种融合测地线的水平集模型(CV-GAC)和图割优化相结合的算法。采用高斯混合模型和背景差分获取目标的运动区域,在运动区域内自动设置初始曲线的轮廓,对运动区域进行数学形态学运算,利用融合测地线的水平集模型自适应处理目标的拓扑变化,并用图割进行能量函数的最优化。实验结果表明,与其他传统方法相比,缩短了运动目标分割的时间,能够正确和快速提取运动目标的活动轮廓。
  • [1] Moreno J C, Prasath V B S, Proença H, et al. Fast and globally convex multiphase active contours for brain MRI Segmentation[J]. Computer Vision and Image Understanding, 2014,125:237-250
    [2] Alexiadis D S, Sergiadis G D. Motion estimation, segmentation and separation, using hypercomplex phase correlation, clustering techniques and graph-based optimization[J]. Computer Vision and Image Understanding, 2009,113(2):212-234
    [3] Rashwan H A, Puig D, Garcia M A. Improving the robustness of variational optical flow through tensor voting[J]. Computer Vision and Image Understanding, 2012,116(9):953-966
    [4] Karavasilis V, Blekas K, Nikou C. A novel framework for motion segmentation and tracking by clustering incomplete trajectories[J]. Computer Vision and Image Understanding, 2012,116(11):1135-1148
    [5] Cremers D, Rousson M, Deriche R. A review of statistical approaches to level set segmentation:integrating color, texture, motion and shape[J]. International Journal of Computer Vision, 2007,72(2):195-215
    [6] Osher S, Sethian J A. Fronts propagating with curvature-dependent speed:algorithms based on Hamilton-Jacobi formulations[J]. Journal of Computational Physics, 1988,79(1):12-49
    [7] Paragios N, Deriche R. Geodesic active contours and level sets for the detection and tracking of moving objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(3):266-280
    [8] Fussenegger M, Roth P, Bischof H, et al. A level set framework using a new incremental, robust Active Shape Model for object segmentation and tracking[J]. Image and Vision Computing, 2009,27(8):1157-1168
    [9] Ren C Y, Prisacariu V, Reid I. Regressing local to global shape properties for online segmentation and tracking[J]. International Journal of Computer Vision, 2014,106(3):269-281
    [10] Sobral A, Vacavant A. A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos[J]. Computer Vision and Image Understanding, 2014,122:4-21
    [11] Tao W B, Tai X C. Multiple piecewise constant with geodesic active contours (MPC-GAC) framework for interactive image segmentation using graph cut optimization[J]. Image and Vision Computing, 2011,29(8):499-508
    [12] Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001,23(11):1222-1239
    [13] El-Zehiry N, Sahoo P, Elmaghraby A. Combinatorial Optimization of the piecewise constant Mumford-Shah functional with application to scalar/vector valued and volumetric image segmentation[J]. Image and Vision Computing, 2011,29(6):365-381
    [14] El-Zehiry N, Xu S, Sahoo P, et al. Graph cut optimization for the Mumford-Shah model[C]//The Seventh IASTED International Conference on Visualization, Imaging and Image Processing. Anaheim, CA, USA:ACTA Press, 2007:182-187
    [15] Kolmogorov V, Boykov Y. What metrics can be approximated by geo-cuts, or global optimization of length/area and flux[C]//Proceedings of the Tenth IEEE International Conference on Computer Vision, Beijing:IEEE, 2005:564-571
  • 加载中
计量
  • 文章访问数:  251
  • HTML全文浏览量:  27
  • PDF下载量:  11
  • 被引次数: 0
出版历程
  • 收稿日期:  2015-05-10
  • 刊出日期:  2017-01-16

目录

    /

    返回文章
    返回