Multiscale Detection of Filamentary Features in Image Data

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5207, Issue 2, 2003, Pages 592-606

  Huo, Xiaoming ^a   Chen, Jihong ^a   Donoho, David ^b  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

Curvilinear features; Digital axoids; Digital image; Multiscale detection of filamentary structures

Indexed keywords

COMPUTER SIMULATION; DATA REDUCTION; DIGITAL SIGNAL PROCESSING; IMAGE PROCESSING; RADAR TARGET RECOGNITION; RANDOM PROCESSES; SPURIOUS SIGNAL NOISE;

CURVILINEAR FEATURES; DIGITAL AXOIDS; DIGITAL IMAGE; MULTISCALE DETECTION OF FILAMENTARY STRUCTURES;

FEATURE EXTRACTION;

EID: 1242263473     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.503721     Document Type: Conference Paper

References (4)

1. E. Arias-Castro, D.L. Donoho, and X. Huo (2003). Asymptotically optimal detection of geometric objects by fast multiscale methods. Working paper.
2. E. Arias-Castro, D.L. Donoho, and X. Huo (2003). Adaptive multiscale detection of filamentary structures embedded in a background of Uniform random points. Working paper.
3. E. Arias-Castro, D.L. Donoho, X. Huo, and C. Tovey (2003). Connect-The-Dots: How many random points can a regular curve pass through? (Work in progress.)
4. D.L. Donoho and X. Huo (2002). Beamlets and multiscale image analysis. In: T.J. Barth, T. Chan, and R. Haimes (Eds.), Springer Lecture Notes in Computational Science and Engineering 20, 149-196.