Unified Computation of Strict Maximum Likelihood for Geometric Fitting |
| |
Authors: | Kenichi Kanatani Yasuyuki Sugaya |
| |
Affiliation: | 1.Department of Computer Science,Okayama University,Okayama,Japan;2.Department of Computer Science and Engineering,Toyohashi University of Technology,Aichi,Japan |
| |
Abstract: | A new numerical scheme is presented for computing strict maximum likelihood (ML) of geometric fitting problems having an implicit
constraint. Our approach is orthogonal projection of observations onto a parameterized surface defined by the constraint.
Assuming a linearly separable nonlinear constraint, we show that a theoretically global solution can be obtained by iterative
Sampson error minimization. Our approach is illustrated by ellipse fitting and fundamental matrix computation. Our method
also encompasses optimal correction, computing, e.g., perpendiculars to an ellipse and triangulating stereo images. A detailed
discussion is given to technical and practical issues about our approach. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|