DocumentCode
2406786
Title
Dyadic scale space
Author
Cong, Ge ; Ma, Songde
Author_Institution
Inst. of Autom., Acad. Sinica, Beijing, China
Volume
2
fYear
1996
fDate
25-29 Aug 1996
Firstpage
399
Abstract
We approximate Gaussian function with any scale by linear combination of Gaussian functions with dyadic scales so that scale space can be constructed much more efficiently. The approximation error is so small that our approach can be used widely in computer vision and pattern recognition. Features at any scale can also be found efficiently by tracking from the dyadic scales
Keywords
Gaussian processes; image sampling; state-space methods; Gaussian function; approximation error; computer vision; dyadic scale space; pattern recognition; Automation; Filtering theory; Fourier transforms; Frequency; Interpolation; Kernel; Laboratories; Least squares approximation; Pattern recognition; Sampling methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Conference_Location
Vienna
ISSN
1051-4651
Print_ISBN
0-8186-7282-X
Type
conf
DOI
10.1109/ICPR.1996.546856
Filename
546856
Link To Document