17 March 2017 Reading recognition of pointer meter based on pattern recognition and dynamic three-points on a line
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Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103410K (2017) https://doi.org/10.1117/12.2268429
Event: Ninth International Conference on Machine Vision, 2016, Nice, France
Abstract
Pointer meters are frequently applied to industrial production for they are directly readable. They should be calibrated regularly to ensure the precision of the readings. Currently the method of manual calibration is most frequently adopted to accomplish the verification of the pointer meter, and professional skills and subjective judgment may lead to big measurement errors and poor reliability and low efficiency, etc. In the past decades, with the development of computer technology, the skills of machine vision and digital image processing have been applied to recognize the reading of the dial instrument. In terms of the existing recognition methods, all the parameters of dial instruments are supposed to be the same, which is not the case in practice. In this work, recognition of pointer meter reading is regarded as an issue of pattern recognition. We obtain the features of a small area around the detected point, make those features as a pattern, divide those certified images based on Gradient Pyramid Algorithm, train a classifier with the support vector machine (SVM) and complete the pattern matching of the divided mages. Then we get the reading of the pointer meter precisely under the theory of dynamic three points make a line (DTPML), which eliminates the error caused by tiny differences of the panels. Eventually, the result of the experiment proves that the proposed method in this work is superior to state-of-the-art works.
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Yongqiang Zhang, Mingli Ding, Wuyifang Fu, Yongqiang Li, "Reading recognition of pointer meter based on pattern recognition and dynamic three-points on a line", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410K (17 March 2017); doi: 10.1117/12.2268429; https://doi.org/10.1117/12.2268429
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