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Robust segmentation of visual data using ranked unbiased scale estimate

Published online by Cambridge University Press:  01 November 1999

Alireza Bab-Hadiashar  and
David Suter
Alireza Bab-Hadiashar
Affiliation:
Intelligent Robotics Research Centre, Department of Electrical & Computer Systems Engineering, Monash University, Clayton Vic. 3168 (Australia)[email protected]@eng.monash.edu.au
David Suter
Affiliation:
Intelligent Robotics Research Centre, Department of Electrical & Computer Systems Engineering, Monash University, Clayton Vic. 3168 (Australia)[email protected]@eng.monash.edu.au
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Abstract

A method of data segmentation, based upon robust least K-th order statistical model fitting (LKS), is proposed and applied to image motion and range data segmentation. The estimation method differs from other approaches using versions of LKS in a number of important ways. Firstly, the value of K is not determined by a complex optimization routine. Secondly, having chosen a fit, the estimation of scale of the noise is not based upon the K-th order statistic of the residuals. Other aspects of the full segmentation scheme include the use of segment contiguity to: (a) reduce the number of random sample fits used in the LKS stage, and (b) to “fill-in” holes caused by isolated miss-classified data.

Keywords

Robust segmentationVisual dataScale estimateLKS methodRobust statistic
Type
Research Article
Information
Robotica , Volume 17 , Issue 6 , November 1999 , pp. 649 - 660
Copyright
© 1999 Cambridge University Press

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