MPGC 1991 阅读摘录

ABSTRACT
1. For greater flexibility the geometric constraints are treated as weighted observation equations and not as strict conditions.
2. It is proposed to use a Wallis filter for a radiometric equalisation of the image before matching.
3. The Wallis filter has also been used to enhance contrast, high contrast being necessary for accurate matching.
4. A method to derive reasonable approximations by an image pyramid based (coarse-to-fine) approach is presented.
5. The pyramid approach increases the convergence radius (in practical tests parallaxes up to 70 pixels have been handled), convergence rate and computational speed, and can be exploited for a better quality control and self-adaptivity of the algorithm.
6. Without the image pyramid, convergence has been achieved, in some cases, for errors in the approximate values of up to 10-20 pixels, but optimally these approximate values should be 1 pixel accurate.
7. MPGC is a combination of area-based and feature-based, especially edge-based, matching.
8. The theoretical precision of the shifts, in the case of good targets, typically is 0.01-0.05 pixels. The achieved accuracy was for good planar targets 0.2-1 um, for signalised  or good natural points 2-3 um, and for natural points on general points on general surfaces 10-15 um, whereby the pixel spacing was typically 10 um. In the performed tests, the matching accuracy was generally similar to the accuracy of manual measurements; in certain cases the matching accuracy was even higher than the manual one. The percentage of blunders automatically detected by MPGC varied from 5%-25% of the total number of points, depending on the image content and object surface. The percentage of undetected blunders was 1%-3% of the points accepted by MPGC as being correct, thus comparable to the error rate of a human operator.