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

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

1.
西北工业大学机电学院, 西安 710072

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

计量
- 文章访问数: 238
- HTML全文浏览量: 24
- 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

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

1.
School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China

摘要

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。
- 目标检测 /
- 活动轮廓 /
- 水平集模型 /
- 运动目标 /
- 图割
Abstract: To solve the problem of moving object tracking in video image sequences, this paper presents a novel algorithm that combines the Chan and Vese Geodesic Active Contour (CV-GAC) model with graph cut optimization. Firstly, the object's active contours are obtained by using the Gaussian mixture model and background subtraction; then, the contour of the initial curve is automatically set in the moving area, and the mathematical morphological operation is carried out by using the geodesic level set model to adaptively treat the object's topology change and globally optimize the energy function with the graph cut optimization. The experimental results show that this method effectively shortens the time for the moving object segmentation, correctly and quickly extracting the active contours of moving targets.
- object tracking /
- geodesic active contour /
- geodesic level set model /
- moving object /
- graph cut optimization

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参考文献(15)

[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