DocumentCode :
2395441
Title :
Adaptive linear feature detection based on beamlet
Author :
Shi, Qin-Feng ; Zhang, Yan-Ning
Author_Institution :
Sch. of Comput., Northwestern Polytech. Univ., Xi´´an, China
Volume :
7
fYear :
2004
fDate :
26-29 Aug. 2004
Firstpage :
3981
Abstract :
Linear feature detection is very important in computer vision, image segmentation and pattern recognition. Traditional linear feature detectors based on pixel processing each by each may fail to detect out lines in image with low SNR. A fast discrete beamlet transform and an adaptive method of linear feature detection are proposed, which can detect lines with any orientation, location and length. The scale parameter can be adaptively determined by histogram of beamlet energy function distribution. Experiment results prove the efficiency of the method proposed even in image with very low SNR.
Keywords :
Hough transforms; Radon transforms; discrete wavelet transforms; feature extraction; Hough transforms; Radon transforms; adaptive linear feature detection; beamlet energy function distribution; computer vision; fast discrete beamlet transform; histogram; image segmentation; low SNR; pattern recognition; pixel processing; Computer vision; Digital images; Discrete transforms; Eyes; Feature extraction; Humans; Image processing; Image segmentation; Interpolation; Pixel;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
Type :
conf
DOI :
10.1109/ICMLC.2004.1384534
Filename :
1384534
Link To Document :
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