Performance evaluation of iterative geometric fitting algorithms

Authors:
Kenichi Kanatani;Yasuyuki Sugaya
Affiliations:
Department of Computer Science, Okayama University, Okayama 700-8530, Japan;Department of Information and Computer Sciences, Toyohashi University of Technology, Toyohashi, Aichi 441-8580, Japan
Venue:
Computational Statistics & Data Analysis
Year:
2007

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Abstract

The convergence performance of typical numerical schemes for geometric fitting for computer vision applications is compared. First, the problem and the associated KCR lower bound are stated. Then, three well-known fitting algorithms are described: FNS, HEIV, and renormalization. To these, we add a special variant of Gauss-Newton iterations. For initialization of iterations, random choice, least squares, and Taubin's method are tested. Simulation is conducted for fundamental matrix computation and ellipse fitting, which reveals different characteristics of each method.