Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/1329
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Type: Journal article
Title: From FNS to HEIV: A link between two vision parameter estimation methods
Author: Chojnacki, W.
Brooks, M.
Van Den Hengel, A.
Gawley, D.
Citation: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004; 26(2):264-268
Publisher: IEEE Computer Soc
Issue Date: 2004
ISSN: 0162-8828
1939-3539
Statement of
Responsibility: 
Wojciech Chojnacki, Michael J. Brooks, Anton van den Hengel, and Darren Gawley
Abstract: Problems requiring accurate determination of parameters from imagebased quantities arise often in computer vision. Two recent, independently developed frameworks for estimating such parameters are the FNS and HEIV schemes. Here, it is shown that FNS and a core version of HEIV are essentially equivalent, solving a common underlying equation via different means. The analysis is driven by the search for a nondegenerate form of a certain generalized eigenvalue problem and effectively leads to a new derivation of the relevant case of the HEIV algorithm. This work may be seen as an extension of previous efforts to rationalize and interrelate a spectrum of estimators, including the renormalization method of Kanatani and the normalized eight-point method of Hartley.
Keywords: Statistical methods; maximum likelihood; (un)constrained Minimization; fundamental matrix; epipolar equation
Description: Copyright © 2004 IEEE
RMID: 0020040067
DOI: 10.1109/TPAMI.2004.1262197
Appears in Collections:Computer Science publications

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