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Proceedings Paper

Multiscale detection of filamentary features in image data
Author(s): Xiaoming Huo; Jihong Chen; David L. Donoho
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Paper Abstract

Taking advantage of the new developments in mathematical statistics, a multiscale approach is designed to detect filament or filament-like features in noisy images. The major contribution is to introduce a general framework in cases when the data is digital. Our detection method can detect the presence of an underlying curvilinear feature with the lowest possible strength that are still detectible in theory. Simulation results on synthetic data will be reported to illustrate its effectiveness in finite digital situations.

Paper Details

Date Published: 13 November 2003
PDF: 15 pages
Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); doi: 10.1117/12.503721
Show Author Affiliations
Xiaoming Huo, Georgia Institute of Technology (United States)
Jihong Chen, Georgia Institute of Technology (United States)
David L. Donoho, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 5207:
Wavelets: Applications in Signal and Image Processing X
Michael A. Unser; Akram Aldroubi; Andrew F. Laine, Editor(s)

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