Adaptive linear feature detection based on beamlet

Qin Feng Shi; Yan Ning Zhang

Adaptive linear feature detection based on beamlet

Qin Feng Shi, Yan Ning Zhang

School of Computer Science

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Pages	3981-3984
Number of pages	4
State	Published - 2004
Event	Proceedings of 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China Duration: 26 Aug 2004 → 29 Aug 2004

Publication series

Name	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Volume	7

Conference

Conference	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Shanghai
Period	26/08/04 → 29/08/04

Keywords

Beamlet transform
Hough transform
Linear feature detection
Radon transform
Wavelet transform

Cite this

@inproceedings{c0ac8da9739145b79e15e67d18e01ac3,

title = "Adaptive linear feature detection based on beamlet",

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 = "Beamlet transform, Hough transform, Linear feature detection, Radon transform, Wavelet transform",

author = "Shi, {Qin Feng} and Zhang, {Yan Ning}",

year = "2004",

language = "英语",

isbn = "0780384032",

series = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

pages = "3981--3984",

booktitle = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

note = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics ; Conference date: 26-08-2004 Through 29-08-2004",

}

Shi, QF & Zhang, YN 2004, Adaptive linear feature detection based on beamlet. in Proceedings of 2004 International Conference on Machine Learning and Cybernetics. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 7, pp. 3981-3984, Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26/08/04.

Adaptive linear feature detection based on beamlet. / Shi, Qin Feng; Zhang, Yan Ning.
Proceedings of 2004 International Conference on Machine Learning and Cybernetics. 2004. p. 3981-3984 (Proceedings of 2004 International Conference on Machine Learning and Cybernetics; Vol. 7).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Adaptive linear feature detection based on beamlet

AU - Shi, Qin Feng

AU - Zhang, Yan Ning

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

KW - Beamlet transform

KW - Hough transform

KW - Linear feature detection

KW - Radon transform

KW - Wavelet transform

UR - http://www.scopus.com/inward/record.url?scp=6444225231&partnerID=8YFLogxK

M3 - 会议稿件

AN - SCOPUS:6444225231

SN - 0780384032

T3 - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

SP - 3981

EP - 3984

BT - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

T2 - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

Y2 - 26 August 2004 through 29 August 2004

ER -

Adaptive linear feature detection based on beamlet

Abstract

Publication series

Conference

Keywords

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