Title :
Singular values and least squares matching
Author_Institution :
Harvard Univ., USA
Abstract :
In this paper we investigate least squares matching problems, extending the methods of our earlier paper (1989) in such a way as to make them applicable to problems involving sets of points that are so large that approximate answers are of interest. These problems are formulated in terms of continuous descent equations, and lower bounds on the quality of the best match are obtained in terms of the singular values of certain matrices determined directly by the data
Keywords :
image matching; least squares approximations; singular value decomposition; best match quality; continuous descent equations; image matching; least squares matching; lower bounds; matrices; singular values; Computer vision; Equations; Genetic mutations; Laboratories; Least squares methods;
Conference_Titel :
Decision and Control, 1997., Proceedings of the 36th IEEE Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-4187-2
DOI :
10.1109/CDC.1997.657597
