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|Title:||From FNS to HEIV: A link between two vision parameter estimation methods|
Van Den Hengel, A.
|Citation:||IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004; 26(2):264-268|
|Publisher:||IEEE Computer Soc|
|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|
|Appears in Collections:||Computer Science publications|
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