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L ∞ minimization in geometric reconstruction problems

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Date

2004

Authors

Hartley, Richard
Schaffalitzky, Frederik

Journal Title

Journal ISSN

Volume Title

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

We investigate the use of the L∞ cost function in geometric vision problems. This cost function measures the maximum of a set of model-fitting errors, rather than the sum-of-squares, or L 2 cost function that is commonly used (in least-squares fitting).

Description

Keywords

Keywords: Cost functions; Image sequences; Motion recovery; Projection errors; Cameras; Computer software; Data reduction; Matrix algebra; Optimization; Polynomials; Problem solving; Computer vision

Citation

URI

http://hdl.handle.net/1885/79556

Collections

ANU Research Publications

Source

Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Type

Conference paper

Book Title

Entity type

Access Statement

License Rights

DOI

Restricted until

2037-12-31

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