An M-estimator for High Breakdown Robust Estimation in Computer Vision

Hoseinnezhad, R and Bab-Hadiashar, A 2011, 'An M-estimator for High Breakdown Robust Estimation in Computer Vision', Computer Vision and Image Understanding, vol. 115, no. 8, pp. 1145-1156.

Document type: Journal Article
Collection: Journal Articles

Title An M-estimator for High Breakdown Robust Estimation in Computer Vision
Author(s) Hoseinnezhad, R
Bab-Hadiashar, A
Year 2011
Journal name Computer Vision and Image Understanding
Volume number 115
Issue number 8
Start page 1145
End page 1156
Total pages 12
Publisher Elsevier
Abstract Several high breakdown robust estimators have been developed to solve computer vision problems involving parametric modeling and segmentation of multi-structured data. Since the cost functions of these estimators are not differentiable functions of parameters, they are commonly optimized by random sampling. This random search can be computationally cumbersome in cases involving segmentation of multiple structures. This paper introduces a high breakdown M-estimator (called HBM for short) with a differentiable cost function that can be directly optimized by iteratively reweighted least squares regression. The fast convergence and high breakdown point of HBM make this estimator an outstanding choice for segmentation of multi-structured data. The results of a number of experiments on range image segmentation and fundamental matrix estimation problems are presented. Those experiments involve both synthetic and real image data and benchmark the performance of HBM estimator both in terms of accurate segmentation of numerous structures in the data and convergence speed in comparison against a number of modern robust estimators developed for computer vision applications (e.g. pbM and ASKC). The results show that HBM outperforms other estimators in terms of computation time while exhibiting similar or better accuracy of estimation and segmentation.
Subject Computer Vision
Control Systems, Robotics and Automation
Image Processing
Keyword(s) Image motion analysis
Image segmentation
Optimization methods
Parameter estimation
DOI - identifier 10.1016/j.cviu.2011.03.007
Copyright notice © 2011 Elsevier Inc. All rights reserved.
ISSN 10773142
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
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