dc.contributor.author |
Haralick, Robert |
en |
dc.date.accessioned |
2008-08-21T01:56:22Z |
en |
dc.date.available |
2008-08-21T01:56:22Z |
en |
dc.date.issued |
1998 |
en |
dc.identifier.citation |
Communication and Information Technology Research Technical Report 29, (1998) |
en |
dc.identifier.issn |
1178-3716 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/2746 |
en |
dc.description |
You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the original CITR web site; http://citr.auckland.ac.nz/techreports/ under terms that include this permission. All other rights are reserved by the author(s). |
en |
dc.description.abstract |
This paper describes how to propagate approximately additive random perturbations through any kind of vision algorithm step in which the appropriate random perturbation model for the estimated quantity produced by the vision step is also an additive random perturbation. We assume that the vision algorithm step can be modeled as a calculation (linear or non-linear) that produces an estimate that minimizes an implicit scaler function of the input quantity and the calculated estimate. The only assumption is that the scaler function be non-negative, have finite first and second partial derivatives, that its value is zero for ideal data, and that the random perturbations are small enough so that the relationship between the scaler function evaluated at the ideal but unknown input and output quantities and evaluated at the observed input quantity and perturbed output quantity can be approximated sufficiently well by a first order Taylor series expansion. The paper finally discusses the issues of verifying that the derived statistical behavior agrees with the experimentally observed statistical behavior. |
en |
dc.publisher |
CITR, The University of Auckland, New Zealand |
en |
dc.relation.ispartofseries |
Communication and Information Technology Research (CITR) Technical Report Series |
en |
dc.rights |
Copyright CITR, The University of Auckland. You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the original CITR web site under terms that include this permission. All other rights are reserved by the author(s). |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.source.uri |
http://citr.auckland.ac.nz/techreports/1998/CITR-TR-29.pdf |
en |
dc.title |
Propagating Covariance in Computer Vision |
en |
dc.type |
Technical Report |
en |
dc.subject.marsden |
Fields of Research::280000 Information, Computing and Communication Sciences |
en |