Title :
Dyadic scale space
Author :
Cong, Ge ; Ma, Songde
Author_Institution :
Inst. of Autom., Acad. Sinica, Beijing, China
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;
Conference_Titel :
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Conference_Location :
Vienna
Print_ISBN :
0-8186-7282-X
DOI :
10.1109/ICPR.1996.546856
