基于像素筛选技术的光流估计方法

›› 2012, Vol. 42 ›› Issue (04): 979-984.

基于像素筛选技术的光流估计方法

马龙, 王鲁平, 李飚, 陈小天

国防科技大学 ATR重点实验室, 长沙 410073

收稿日期:2011-05-12 出版日期:2012-07-01 发布日期:2012-07-01
基金资助:
国防预研基金项目.

Pixel-filtrated based method for determining optical flow

MA Long, WANG Lu-ping, LI Biao, CHEN Xiao-tian

ATR Key Laboratory, National University of Defense Technology, Changsha 410073, China

Received:2011-05-12 Online:2012-07-01 Published:2012-07-01

摘要/Abstract

摘要： 针对传统的光流估计方法对光照变化敏感,且在物体边缘处难以获得准确的光流估计的问题,提出了一种基于像素筛选技术的光流估计方法。该方法在光变环境下定义了基本方程,并采用了改进的局部光流技术。新的局部光流技术首先对局部窗口内的像素进行两次筛选,然后基于保留像素采用加权最小二乘法估计光流。光变、噪声环境下的测试实验证明了该方法的有效性和鲁棒性。

关键词: 计算机应用, 光流, 边缘, 光照变化, 噪声

Abstract: The traditional methods for determining optical flow are not robust under variable illumination, and do not work well at object edges. To overcome these shortcomings, a pixel-filtration based method is proposed. The method defines a new basic equation under variable illumination, and uses an improved local flow technique. First, this technique selects proper pixels twice from the local window; then, it determines the flow using the weighted-least-square method based on the selected pixels. Experiment results under variable illumination and noise demonstrate the validity and robust of the proposed method.

Key words: computer application, optical flow, object edge, variable illumination, noise

中图分类号:

TP391.41

马龙, 王鲁平, 李飚, 陈小天. 基于像素筛选技术的光流估计方法[J]. , 2012, 42(04): 979-984.

MA Long, WANG Lu-ping, LI Biao, CHEN Xiao-tian. Pixel-filtrated based method for determining optical flow[J]. , 2012, 42(04): 979-984.

参考文献

[1] Axel Besinger, Tamara Sztynda, Saroj Lal, et al. Optical flow based analyses to detect emotion from human facial image data[J]. Expert Systems with Applications, 2010, 37(12):8897-8902.
[2] Lee Dah-Jye, Merrell Paul, Wei Zhao-yi, et al. Two-frame structure from motion using optical flow probability distributions for unmanned air vehicle obstacle avoidance[J]. Machine Vision and Applications, 2010, 21(3):229-240.
[3] Kai Guo, Ishwar P, Konrad J, et al. Action recognition using sparse representation on covariance manifolds of optical flow//IEEE International Conference on AVSS, Boston, MA, 2010:188-195.
[4] Horn B K, Schunck B, Schunck Brian G. Determining optical flow[J]. Artificial Intelligence, 1981, 17(1):185-203.
[5] Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision//Proc DARPA Image Understanding Workshop, 1981:121-130.
[6] Nagel H H, Enkelmann W. An investigation of smoothness constraints for the estimation of displacement vector field from image sequences[J]. IEEE Transactions on Pattern Analyze and Machine Intelligence, 1986, 8:565-593.
[7] Black M J, Anandan P. The robust estimation of multiple motions:parametric and piecewise smooth flow fields[J]. Computer Vision and Image Understanding, 1996, 63:75-104.
[8] Brox Thomas, Malik Jitendra. Large displacement optical flow:descriptor matching in variational motion estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(3):500-513.
[9] Kim Yeon-Ho, Kak Avinash C. Error analysis of robust optical flow estimation by least median of squares method for the varying illumination model[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28 (9):1418-1435.
[10] Lai Shang-Hong, Fang Ming. Robust and efficient image alignment with spatially varying illumination models//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999:167-172.
[11] Barron J L, Fleet D J, Beauchemin S S. Performance of optical flow techniques[J]. International Journal of Computer Vision, 1994, 12 (1):43-77.
[12] Rousseeuw Peter J. Least median of squares regression[J]. Journal of the American Statistical Association, 1984, 79(388):871-880.

相关文章 15

[1]	刘富,宗宇轩,康冰,张益萌,林彩霞,赵宏伟. 基于优化纹理特征的手背静脉识别系统[J]. 吉林大学学报(工学版), 2018, 48(6): 1844-1850.
[2]	王利民,刘洋,孙铭会,李美慧. 基于Markov blanket的无约束型K阶贝叶斯集成分类模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1851-1858.
[3]	金顺福,王宝帅,郝闪闪,贾晓光,霍占强. 基于备用虚拟机同步休眠的云数据中心节能策略及性能[J]. 吉林大学学报(工学版), 2018, 48(6): 1859-1866.
[4]	赵东,孙明玉,朱金龙,于繁华,刘光洁,陈慧灵. 结合粒子群和单纯形的改进飞蛾优化算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1867-1872.
[5]	刘恩泽,吴文福. 基于机器视觉的农作物表面多特征决策融合病变判断算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1873-1878.
[6]	李居朋,张祖成,李墨羽,缪德芳. 基于Kalman滤波的电容屏触控轨迹平滑算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1910-1916.
[7]	欧阳丹彤, 范琪. 子句级别语境感知的开放信息抽取方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1563-1570.
[8]	刘富, 兰旭腾, 侯涛, 康冰, 刘云, 林彩霞. 基于优化k-mer频率的宏基因组聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1593-1599.
[9]	桂春, 黄旺星. 基于改进的标签传播算法的网络聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1600-1605.
[10]	刘元宁, 刘帅, 朱晓冬, 陈一浩, 郑少阁, 沈椿壮. 基于高斯拉普拉斯算子与自适应优化伽柏滤波的虹膜识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1606-1613.
[11]	车翔玖, 王利, 郭晓新. 基于多尺度特征融合的边界检测算法[J]. 吉林大学学报(工学版), 2018, 48(5): 1621-1628.
[12]	赵宏伟, 刘宇琦, 董立岩, 王玉, 刘陪. 智能交通混合动态路径优化算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[13]	黄辉, 冯西安, 魏燕, 许驰, 陈慧灵. 基于增强核极限学习机的专业选择智能系统[J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[14]	傅文博, 张杰, 陈永乐. 物联网环境下抵抗路由欺骗攻击的网络拓扑发现算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236.
[15]	曹洁, 苏哲, 李晓旭. 基于Corr-LDA模型的图像标注方法[J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed