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From FNS to HEIV: a link between two vision parameter estimation methods

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4 Author(s)
W. Chojnacki ; Sch. of Comput. Sci., Adelaide Univ., SA, Australia ; M. J. Brooks ; A. van den Hengel ; D. Gawley

Problems requiring accurate determination of parameters from image-based 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.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:26 ,  Issue: 2 )