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Sensors 2016, 16(12), 2006; doi:10.3390/s16122006

Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical Morphology

1
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
2
National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China
3
Department of Computer Science, University of York, York YO10 5GH, UK
4
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 1 October 2016 / Revised: 15 November 2016 / Accepted: 23 November 2016 / Published: 26 November 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [593 KB, uploaded 26 November 2016]   |  

Abstract

A point machine’s gap is an important indication of its healthy status. An edge detection algorithm is proposed to measure and calculate a point machine’s gap from the gap image captured by CCD plane arrays. This algorithm integrates adaptive wavelet-based image denoising, locally adaptive image binarization, and mathematical morphology technologies. The adaptive wavelet-based image denoising obtains not only an optimal denoising threshold, but also unblurred edges. Locally adaptive image binarization has the advantage of overcoming the local intensity variation in gap images. Mathematical morphology may suppress speckle spots caused by reflective metal surfaces in point machines. The subjective and objective evaluations of the proposed method are presented by using point machine gap images from a railway corporation in China. The performance between the proposed method and conventional edge detection methods has also been compared, and the result shows that the former outperforms the latter. View Full-Text
Keywords: edge detection; wavelet-based image denoising; image binarization; mathematical morphology edge detection; wavelet-based image denoising; image binarization; mathematical morphology
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xu, T.; Wang, G.; Wang, H.; Yuan, T.; Zhong, Z. Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical Morphology. Sensors 2016, 16, 2006.

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