Robust Computation of Optical Flow using Orientation Code Matching

A robust matching scheme for computing optical flow within a sequence of grayscale images is proposed. The technique employs the gradient information in textured images for extracting features in the form of orientation codes, which are then used for matching. The proposed method has been found to be robust in cases of matching under different ill-conditionings especially illumination variations. We utilize its robustness to compute optical flow in cases where illumination fluctuation is a problem and matching pixel brightness can introduce errors. Results of computation of optical flow field on real world scenes in the cases of translation, rotation and zooming have been presented and compared with other region matching techniques.

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

[2]  Gilad Adiv,et al.  Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  K. Prazdny,et al.  Motion and Structure from Optical Flow , 1979, IJCAI.

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

[5]  Ajit Singh,et al.  An estimation-theoretic framework for image-flow computation , 1990, [1990] Proceedings Third International Conference on Computer Vision.

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

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

[8]  Steven S. Beauchemin,et al.  The computation of optical flow , 1995, CSUR.

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

[10]  William B. Thompson,et al.  Disparity Analysis of Images , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Ta Camus,et al.  Real-time quantized optical flow , 1995, Proceedings of Conference on Computer Architectures for Machine Perception.

[12]  Brian G. Schunck,et al.  Image Flow Segmentation and Estimation by Constraint Line Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  J. Barron,et al.  Optical flow to measure minute increments in plant growth , 1994 .

[14]  Patrick Bouthemy,et al.  Computation and analysis of image motion: A synopsis of current problems and methods , 1996, International Journal of Computer Vision.

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