Zl. Li et al., Robust surface matching for automated detection of local deformations using least-median-of-squares estimator, PHOTOGR E R, 67(11), 2001, pp. 1283-1292
Automated detection of the local deformation of a surface involves the dete
ction of the differences between an original and a deformed digital surface
model without the aid of control points. The process is normally automated
by matching two digital surface models. This technique is desirable for ma
ny industrial applications.
With the existence of local deformation, conventional surface matching algo
rithms with least-squares conditions will fail because the estimated parame
ters are influenced by local deformation. As a result, some robust estimato
rs can be applied to robustify surface matching algorithms, In addition to
a re-evaluation of the performance of the M-estimator, two other robust est
imators-the GM-estimator and the Lms-estimator (least median of squares)-ha
ve been explored in this study for the purpose of detecting local deformati
on. Test results show that the Lms-estimator is superior to both the M-esti
mator and the GM-estimator for detecting local deformation in three respect
s: (1) it is not sensitive to the location of local deformation; (2) the la
rgest tolerable deformation percentage is improved to a level of almost 50
percent; and (3) when the deformation percentage is less than 40 percent, d
eformations of very small magnitude can be detected. It has also been found
that the largest tolerable deformation percentage is related to the magnit
ude of the deformation.