Performance evaluation of iterative geometric fitting algorithms
نویسندگان
چکیده
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. c ©2007 Published by Elsevier B.V. All rights reserved.
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ورودعنوان ژورنال:
- Computational Statistics & Data Analysis
دوره 52 شماره
صفحات -
تاریخ انتشار 2007