Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-28T06:08:30.656Z Has data issue: false hasContentIssue false
  • English
  • Français

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)ali.bab-hadiashar@eng.monash.edu.aud.suter@eng.monash.edu.au
David Suter
Affiliation:
Intelligent Robotics Research Centre, Department of Electrical & Computer Systems Engineering, Monash University, Clayton Vic. 3168 (Australia)ali.bab-hadiashar@eng.monash.edu.aud.suter@eng.monash.edu.au
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)