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基于结构相关的变分光流计算方法 被引量:1

Calculus of Variational Optical Flow Based on Structure-dependent Regularization
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摘要 传统的变分光流计算方法主要是基于各种特征守恒假设和平滑性假设,对图像中的噪声、边缘、阴影、光照变化等方面的处理存在很大误差,尤其是边缘和光照变化会使计算结果产生严重误差。本文提出一种改进的变分光流计算模型,数据项采用灰度守恒与梯度守恒结合约束来解决光照变化问题。引入与结构相关的自适应平滑项,通过减弱平滑项在运动边缘的作用力度,加大梯度惩罚力度,从而有效地抑制运动物体边缘处光流计算的"漂移"现象。实验证明了模型的有效性。 The traditional calculus of variational optical flow is mainly based on various characteristic constancy assumptions and smoothness assumptions.There are serious errors in dealing with image noise,edges,shadow,illumination changes,especially the edges and illumination changes.An improved calculus of variational optical flow is proposed in this paper.The data term is to solve the problem of illumination changes by the combination of gray and gradient constancy assumption.The smoothness term related with structure-dependent regularization is introduced,which can inhibit the edges of moving objects in the "drift" phenomenon effectively by weakening the efforts of smooth term in the boundaries of the moving objects and increasing the punishment of gradient.Experimental results show that this calculus is suitable and effective.
机构地区 海军 南昌航空大学
出处 《南昌航空大学学报(自然科学版)》 CAS 2012年第3期42-47,共6页 Journal of Nanchang Hangkong University(Natural Sciences)
基金 江西省自然科学基金(2009GZS1109)
关键词 变分 光流 光照变化 结构相关 variational optical flow illumination changes structure-dependent
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参考文献12

  • 1桂本烨,钱徽,朱淼良.一种优化梯度计算的改进HS光流算法[J].中国图象图形学报,2005,10(8):1052-1058. 被引量:9
  • 2Horn B K P, Schunck B G. Determining opticalflow[J]. Artificial Intelligence, 1981, 17(1-3) : 185-204.
  • 3Barron J L, Fleet D J, Beauchemin S S. Performance of optical flow techniques [ J ]. International Journal of Computer Vision, 1994, 12(1) :43-77.
  • 4Bruhn A, Weickert J. Towards ultimate motion estimation: Combining highest accuracy with Real- Time performance [ C ]. In Proc. 10th International Conference on Computer Vision, Beijing, China: IEEE Press, 2005: 749-755.
  • 5Brox T, Bruhn A, Papenberg N, et al. High accuracy optic flow estimation based on a theory for warping[ C ]. In Proc. 8th Euro- pean Conference on Computer Vision, Prague, Czech Republic: IEEE Press , 2004: 25-36.
  • 6Baker S, Scharstein D, Lewis J P. A database and evaluation methodology for optical flow[ C]. In Proc. 11th International Con- ference on Computer Vision, Rio de Janeiro, Brazil: IEEE Press, 2009, 07(10) :1-8.
  • 7陈震,高满屯,沈允文.图象光流场计算技术研究进展[J].中国图象图形学报(A辑),2002,7(5):434-439. 被引量:31
  • 8卢宗庆.运动图像分析中的光流计算方法研究[D].西安:西安电子科技大学,2008.
  • 9Bruhn A, Weickert J. LucaslKanade meets HornlSchunck: Combining local and global optic flow methods [ J ]. International Jour- nal of Computer Vision, 2005, 61 ( 3 ) : 211-231.
  • 10Papenberg N. Highly accurate optic flow computation with theoretically justified warping [ J ]. International Journal of Computer Vision, 2006, 67(2):141-158.

二级参考文献9

  • 1王润生.图象理解[M].北京:国防科技大学出版社,1994..
  • 2章毓晋.图象理解与计算机视觉.图象工程(下册)[M].北京:清华大学出版社,2000..
  • 3Beauchemin S S, Barton J L. The computation of optical flow [J].ACM Computing Surveys(CSUR) , 1995,27 ( 3 ) : 433 - 466.
  • 4Horn B K P, Sehunek B G. Determining optical flow[J]. Artificial Intelligence, 1981, 17 ( 1-3 ) : 185 - 204.
  • 5Barton J, Klette R. Quantitative color optical flow [A]. In:Proceedings of the International Conference on Pattern Recognition[C]. Vancouver, Canada, 2002,4:251 - 255.
  • 6Barton J L, Fleet D J, Beauchemin S S, et al. Performance of optical flow techniques [J]. Computer Vision and Pattern Recognition,1992,15(18) : 236 - 242.
  • 7杨杨,张田文.一种基于特征光流的运动目标跟踪方法[J].宇航学报,2000,21(2):8-15. 被引量:21
  • 8林通,石青云.一种基于边缘生长的灰度和彩色图象分割方法[J].中国图象图形学报(A辑),2000,5(11):911-915. 被引量:44
  • 9傅洁,吴立德.计算机视觉中运动分析的连续处理方法综述[J].模式识别与人工智能,1991,4(1):91-99. 被引量:7

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  • 1Negahdaripour S. Revised definition of optical flow: Integration of radiometric and geometric cues for dynamic scene analysis [J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 1998, 20(9): 961- 979.
  • 2Mattavelli M,Nicoulin A. Motion estimation relaxing the constancy brightness eonstraint[C]// IEEE Inter. Conf. Image Processing, 1994(2): 770-774.
  • 3Kim Y H,Martinez A M, Kak A C. Robust motion estimation under varying illumination[J]. Image and Vision Computing, 2005, 23(4): 365-375.
  • 4Black M J,Fleet D J, Yaeoob Y. Robustly estimating changes in image appearance[J]. Computer Vision and Image Understanding, 2000, 78(1): 8-31.
  • 5Brox T,Bruhn A, Papenberg N, et al. High Accuracy Optical Flow Estimation Based on a Theory For Warping[M]. Berlin: Springer Berlin Heidelberg, 2004: 25-36.
  • 6PapenhergN, Bruhn A, Brox T, et al. Highly accurate optic flow computation with theoretically justified warping [J]. Inter. J. Computer Vision, 2006, 67(2) : 141-158.
  • 7Fleet D J, Jepson A D. Computation of component image velocity from local phase information[J]. Inter. J. Computer Vision, 1990, 5(1): 77-104.
  • 8David J. Fleet. Measurement of Image Velocity[M]. Kluwer : Kluwer Academic Publishers Norwell, 1992.
  • 9Gautama T. A phase-based approach to the estimation of the optical flow field using spatial filtering[J]. IEEE Trans. on Neural Networks, 2002, 13 (5): 1127-1136.
  • 10Abhishek K, Frederick T, Alexander W. A decoupled approach to illumination-robust optical flow estimation[J]. IEEE Trans. on Image Proc. , 2013,22 (10) :4136-4147.

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