Authors: Kenichi Kanatani Yasuyuki Sugaya
Publish Date: 2010/05/27
Volume: 38, Issue: 1, Pages: 1-13
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 eg perpendiculars to an ellipse and triangulating stereo images A detailed discussion is given to technical and practical issues about our approach
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