Optical flow estimation and segmentation through surface fitting and robust statistics
UNSPECIFIED (2003) Optical flow estimation and segmentation through surface fitting and robust statistics. In: IEEE International Conference on Systems, Man and Cybernetics (SMC 03), OCT 05-08, 2003, WASHINGTON, D.C..Full text not available from this repository.
This paper presents a method for optical flow estimation and segmentation through polynomial surface fitting, robust least-median-squares regression, and robust statistic clustering mean shift. This approach consists of three stages. First, a standard polynomial surface fitting is used to smooth an image, and least-median-of-squares (LMedS) robust regression is used to calculate the optical flow, which can tolerate up to 50% outlier contamination. Second, the estimated optical flow map is segmented through a mean shift technique. Third, an affine flow model is employed to fit the coarse flow estimates within the segmented regions, and the affine fitted motion of the regions is refined with a robust least median squares process based on optical flow constraints. The experimental results have demonstrated that our approach achieved good performance in most synthetic and real video sequences.
|Item Type:||Conference Item (UNSPECIFIED)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software|
|Series Name:||IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, CONFERENCE PROCEEDINGS|
|Journal or Publication Title:||2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-5, CONFERENCE PROCEEDINGS|
|Number of Pages:||6|
|Page Range:||pp. 1390-1395|
|Title of Event:||IEEE International Conference on Systems, Man and Cybernetics (SMC 03)|
|Location of Event:||WASHINGTON, D.C.|
|Date(s) of Event:||OCT 05-08, 2003|
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