Optical Flow Estimation

This chapter provides a tutorial introduction to gradient-based optical flow estimation. We discuss least-squares and robust estimators, iterative coarse-to-fine refinement, different forms of parametric motion models, different conservation assumptions, probabilistic formulations, and robust mixture models.

[1]  Allan D. Jepson,et al.  The Computational Perception of Scene Dynamics , 1997, Comput. Vis. Image Underst..

[2]  Allan D. Jepson,et al.  Computational Perception of Scene Dynamics , 1996, ECCV.

[3]  Eero P. Simoncelli.  Distributed representation and analysis of visual motion , 1993 .

[4]  David J. Fleet,et al.  Design and Use of Linear Models for Image Motion Analysis , 2000, International Journal of Computer Vision.

[5]  Gunnar Farnebäck.  Very high accuracy velocity estimation using orientation tensors , 2001, ICCV 2001.

[6]  Eero P. Simoncelli,et al.  Differentiation of discrete multidimensional signals , 2004, IEEE Transactions on Image Processing.

[7]  A. Verri,et al.  A computational approach to motion perception , 1988, Biological Cybernetics.

[8]  Joachim Weickert,et al.  Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods , 2005, International Journal of Computer Vision.

[9]  Michael J. Black,et al.  Mixture models for optical flow computation , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Harpreet S. Sawhney,et al.  Layered representation of motion video using robust maximum-likelihood estimation of mixture models and MDL encoding , 1995, Proceedings of IEEE International Conference on Computer Vision.

[11]  Edward H. Adelson,et al.  A unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of models , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  D. Ruppert.  Robust Statistics: The Approach Based on Influence Functions , 1987 .

[13]  Gregory D. Hager,et al.  Efficient Region Tracking With Parametric Models of Geometry and Illumination , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Randal C. Nelson,et al.  Qualitative recognition of motion using temporal texture , 1992, CVGIP Image Underst..

[15]  David J. Fleet,et al.  Velocity Likelihoods in Biological and Machine Vision , 2001 .

[16]  Michael J. Black,et al.  EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, International Journal of Computer Vision.

[17]  Christoph Schnörr,et al.  Determining optical flow for irregular domains by minimizing quadratic functionals of a certain class , 1991, International Journal of Computer Vision.

[18]  Johan Wiklund,et al.  Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  David J. Fleet,et al.  Likelihood functions and confidence bounds for total-least-squares problems , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[20]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[21]  Allan D. Jepson,et al.  Subspace methods for recovering rigid motion I: Algorithm and implementation , 2004, International Journal of Computer Vision.

[22]  Richard Szeliski,et al.  Spline-Based Image Registration , 1997, International Journal of Computer Vision.

[23]  William H. Warren,et al.  Chapter 8 – Self-Motion: Visual Perception and Visual Control , 1995 .

[24]  David J. Fleet,et al.  Stability of Phase Information , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Edward H. Adelson,et al.  Probability distributions of optical flow , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Hans-Hellmut Nagel,et al.  On the Estimation of Optical Flow: Relations between Different Approaches and Some New Results , 1987, Artif. Intell..

[27]  S. Ullman.  The interpretation of structure from motion , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[28]  David Suter,et al.  Robust Optic Flow Computation , 1998, International Journal of Computer Vision.

[29]  P. Anandan,et al.  A computational framework and an algorithm for the measurement of visual motion , 1987, International Journal of Computer Vision.

[30]  A J Ahumada,et al.  Model of human visual-motion sensing. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[31]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[32]  David J. Fleet,et al.  Computation of component image velocity from local phase information , 1990, International Journal of Computer Vision.

[33]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..

[34]  Edward H. Adelson,et al.  Representing moving images with layers , 1994, IEEE Trans. Image Process..

[35]  Hanno Scharr,et al.  Study of Dynamical Processes with Tensor-Based Spatiotemporal Image Processing Techniques , 1998, ECCV.

[36]  G. Farneback.  Very high accuracy velocity estimation using orientation tensors, parametric motion, and simultaneous segmentation of the motion field , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[37]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[38]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[40]  David J. Fleet,et al.  Computing Optical Flow with Physical Models of Brightness Variation , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  P. Anandan,et al.  Factorization with Uncertainty , 2000, International Journal of Computer Vision.

[42]  Michael Spann,et al.  Robust Optical Flow Computation Based on Least-Median-of-Squares Regression , 2004, International Journal of Computer Vision.

[43]  Jitendra Malik,et al.  Robust computation of optical flow in a multi-scale differential framework , 2005, International Journal of Computer Vision.

[44]  M V Srinivasan,et al.  Honeybee navigation: nature and calibration of the "odometer". , 2000, Science.

[45]  David J. Fleet.  Measurement of image velocity , 1992 .

[46]  H. C. Longuet-Higgins,et al.  The interpretation of a moving retinal image , 1980, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[47]  Xie Weixin,et al.  A robust optical flow computation , 2007 .

[48]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[49]  Shahriar Negahdaripour,et al.  Revised Definition of Optical Flow: Integration of Radiometric and Geometric Cues for Dynamic Scene Analysis , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[50]  R. Hetherington.  The Perception of the Visual World , 1952 .

[51]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[52]  Edward H. Adelson,et al.  Motion illusions as optimal percepts , 2002, Nature Neuroscience.

[53]  Stuart Geman,et al.  Statistical methods for tomographic image reconstruction , 1987 .

[54]  Yair Weiss,et al.  Smoothness in layers: Motion segmentation using nonparametric mixture estimation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[55]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.

[56]  E. Adelson,et al.  Phenomenal coherence of moving visual patterns , 1982, Nature.