An adapted Lucas-Kanade's method for optical flow estimation in catadioptric images

The optical flow estimation is one of important problem in computer vision. Differential techniques were used successfully to compute the optical flow in perspective images. Lucas-Kanade is one of the most popular differential method that solve the problem of optical flow by given constrain that motion is locally constant. Even if this method works well for the perspective images, this supposition is less appropriate in the omnidirectional images due to its distortion. In this paper, we propose to use new constraint based on motion model defined for paracatadioptric images. This new constraint will be combined with an adapted neighborhood windows witch are adequate to catadioptric images. We will show in this work that these two hypothesis allows to compute efficiently optical flow from omnidirectional image sequences.

[1]  A. Makadia,et al.  Image processing in catadioptric planes: spatiotemporal derivatives and optical flow computation , 2002, Proceedings of the IEEE Workshop on Omnidirectional Vision 2002. Held in conjunction with ECCV'02.

[2]  Pascal Frossard,et al.  Multiresolution motion estimation for omnidirectional images , 2005, 2005 13th European Signal Processing Conference.

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

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

[5]  Pascal Vasseur,et al.  Markov random fields for catadioptric image processing , 2006, Pattern Recognit. Lett..

[6]  Tomás Svoboda,et al.  Matching in Catadioptric Images with Appropriate Windows, and Outliers Removal , 2001, CAIP.

[7]  Cédric Demonceaux,et al.  Optical flow estimation in omnidirectional images using wavelet approach , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[8]  Shree K. Nayar,et al.  Ego-motion and omnidirectional cameras , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[9]  Brendan McCane,et al.  On Benchmarking Optical Flow , 2001, Comput. Vis. Image Underst..

[10]  Richard Szeliski,et al.  A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[11]  Kostas Daniilidis,et al.  Catadioptric Projective Geometry , 2001, International Journal of Computer Vision.

[12]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[13]  Ryad Benosman,et al.  An Efficient Dynamic Multi-Angular Feature Points Matcher for Catadioptric Views , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.