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Robust Estimation of Optical Flow Based on the Maximum Likelihood Estimators
Kwangho LEE Kwangyoen WOHN
Publication
IEICE TRANSACTIONS on Information and Systems
Vol.E79D
No.9
pp.12861295 Publication Date: 1996/09/25
Online ISSN:
DOI:
Print ISSN: 09168532 Type of Manuscript: PAPER Category: Image Processing,Computer Graphics and Pattern Recognition Keyword: optical flow, image motion, robust statistics, robust estimation, maximum likelihood estimator, iteratively reweighted least squares, robust scale, least absolute residuals estimator,
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Summary:
The robust statistics has recently been adopted by the computer vision community. Various robust approaches in the computer vision research have been proposed in the last decade for analyzing the image motion from the image sequence. Because of the frequent violation of the Gaussian assumption of the noise and the motion discontinuities due to multiple motions, the motion estimates based on the straightforward approaches such as the least squares estimator and the regularization often produces unsatisfactory result. Robust estimation is a promising approach to deal with these problems because it recovers the intrinsic characteristics of the original data with the reduced sensitivity to the contamination. Several previous works exist and report some isolated results, but there has been no comprehensive analysis. In this paper robust approaches to the optical flow estimation based on the maximum likelihood estimators are proposed. To evaluate the performance of the Mestimators for estimating the optical flow, comparative studies are conducted for every possible combinations of the parameters of three types of Mestimators, two types of residuals, two methods of scale estimate, and two types of starting values. Comparative studies on synthetic data show the superiority of the Mestimator of redescending ψfunction using the starting value of least absolute residuals estimator using Huber scale iteration, in comparison with the other Mestimators and least squares estimator. Experimental results from the real image experiments also confirm that the proposed combinations of the Mestimators handle the contaminated data effectively and produce the better estimates than the least squares estimator or the least absolute residuals estimator.

