Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical Morphology
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 bin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-11-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/12/2006 |
| _version_ | 1811306633904521216 |
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| author | Tianhua Xu Guang Wang Haifeng Wang Tangming Yuan Zhiwang Zhong |
| author_facet | Tianhua Xu Guang Wang Haifeng Wang Tangming Yuan Zhiwang Zhong |
| author_sort | Tianhua Xu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:48:59Z |
| format | Article |
| id | doaj.art-8c6724c0ef974aa6ac930c9d02536ef2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:48:59Z |
| publishDate | 2016-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8c6724c0ef974aa6ac930c9d02536ef22022-12-22T02:53:34ZengMDPI AGSensors1424-82202016-11-011612200610.3390/s16122006s16122006Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical MorphologyTianhua Xu0Guang Wang1Haifeng Wang2Tangming Yuan3Zhiwang Zhong4State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaNational Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, ChinaDepartment of Computer Science, University of York, York YO10 5GH, UKSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaA 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.http://www.mdpi.com/1424-8220/16/12/2006edge detectionwavelet-based image denoisingimage binarizationmathematical morphology |
| spellingShingle | Tianhua Xu Guang Wang Haifeng Wang Tangming Yuan Zhiwang Zhong Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical Morphology Sensors edge detection wavelet-based image denoising image binarization mathematical morphology |
| title | Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical Morphology |
| title_full | Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical Morphology |
| title_fullStr | Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical Morphology |
| title_full_unstemmed | Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical Morphology |
| title_short | Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical Morphology |
| title_sort | gap measurement of point machine using adaptive wavelet threshold and mathematical morphology |
| topic | edge detection wavelet-based image denoising image binarization mathematical morphology |
| url | http://www.mdpi.com/1424-8220/16/12/2006 |
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