Title :
Curvilinear Feature Extraction for Noisy Point Pattern Images
Author :
Wang, Haonan ; Lee, Thomas C M
Author_Institution :
Colorado State Univ., Fort Collins
Abstract :
A frequently encountered task in many imaging problems is the detection of curvilinear features hidden in noisy spatial point patterns. This paper investigates the use of principal curves to fulfill this task. The minimum description length principle is applied simultaneously to select the number and to control the smoothness of the principal curves that are required to represent the real features. Practical performance of the proposed approach is demonstrated via numerical experiments.
Keywords :
feature extraction; image processing; smoothing methods; curvilinear feature extraction; minimum description length principle; noisy spatial point pattern images; principal curves; Background noise; Colored noise; Computer vision; Data compression; Data mining; Feature extraction; Image processing; Information retrieval; Machine vision;
Conference_Titel :
Multimedia and Expo, 2007 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-1016-9
Electronic_ISBN :
1-4244-1017-7
DOI :
10.1109/ICME.2007.4284980
