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https://hdl.handle.net/2440/85354
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Type: | Journal article |
Title: | Robust segmentation of visual data using ranked unbiased scale estimate |
Author: | Bab-Hadiashar, A. Suter, D. |
Citation: | Robotica, 1999; 17(6):649-660 |
Publisher: | Cambridge University Press |
Issue Date: | 1999 |
ISSN: | 0263-5747 1469-8668 |
Statement of Responsibility: | Alireza Bab-Hadiashar and David Suter |
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 segmentation; Visual data; Scale estimate; LKS method; Robust statistic |
Rights: | © 1999 Cambridge University Press |
DOI: | 10.1017/S0263574799001812 |
Published version: | http://dx.doi.org/10.1017/s0263574799001812 |
Appears in Collections: | Aurora harvest 2 Computer Science publications |
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