Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical Morphology
AbstractA 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
Share & Cite This Article
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.
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(12):2006.Chicago/Turabian Style
Xu, Tianhua; Wang, Guang; Wang, Haifeng; Yuan, Tangming; Zhong, Zhiwang. 2016. "Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical Morphology." Sensors 16, no. 12: 2006.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.