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

IEEE Transactions on Pattern Analysis and Machine Intelligence
Wojciech ChojnackiDarren Gawley

Abstract

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.

Citations

Jun 27, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·Bogdan Georgescu, Peter Meer
Sep 22, 2006·IEEE Transactions on Pattern Analysis and Machine Intelligence·Bogdan C Matei, Peter Meer

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