Displacement Estimates Through Adaptive Affinities

A technique that is functionally equivalent to the oriented smoothing concept and reduces numerical complexity and computational costs by eliminating the smoothness requirement from the iteration process is introduced. Local affine transformations are applied to propagate uniquely computed flow vectors into homogeneous regions and along edges in a single step. The window within which the local affine transformation is performed can adapt to the local structure of the intensity pattern in accordance with the oriented smoothness concept as formulated by H. Nagel (1987). >

[1]  J J Koenderink,et al.  Depth and shape from differential perspective in the presence of bending deformations. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[2]  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.

[3]  Louis A. Tamburino,et al.  A Unified Approach to the Linear Camera Calibration Problem , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

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

[5]  Hans-Hellmut Nagel,et al.  An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image Sequences , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  W. B. Thompson,et al.  Combining motion and contrast for segmentation , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[8]  K. Prazdny,et al.  On the information in optical flows , 1983, Comput. Vis. Graph. Image Process..

[9]  Anil K. Jain,et al.  Displacement Measurement and Its Application in Interframe Image Coding , 1981, IEEE Trans. Commun..

[10]  Ralph Hartley,et al.  Segmentation of optical flow fields by pyramid linking , 1985, Pattern Recognit. Lett..

[11]  Michael A. Arbib,et al.  Computing the optic flow: The MATCH algorithm and prediction , 1983, Comput. Vis. Graph. Image Process..