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Proceedings Paper

An automatic recognition method of pointer instrument based on improved Hough transform
Author(s): Li Xu; Tian Fang; Xiaoyu Gao
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Paper Abstract

For the automatic recognition of pointer instrument, the method for the automatic recognition of pointer instrument based on improved Hough Transform was proposed in this paper. The automatic recognition of pointer instrument is applied to all kinds of lighting conditions, but the accuracy of it binaryzation will be influenced when the light is too strong or too dark. Therefore, the improved Ostu method was suggested to realize recognition for adaptive thresholding of pointer instrument under all kinds of lighting conditions. On the basis of dial image characteristics, Otsu method is used to get the value of maximum between-cluster variance and initial threshold than analyze its maximum between-cluster variance value to determine the light and shade of the image. When the images are too bright or too dark, the smaller pixels should be given up and then calculate the initial threshold by Otsu method again and again until the best binaryzation image was obtained. Hence, transform the pointer straight line of the binaryzation image to Hough parameter space through improved Hough Transform to determine the position of the pointer straight line by searching the maximum value of arrays of the same angle. Finally, according to angle method, the pointer reading was obtained by the linear relationship for the initial scale and angle of the pointer instrument. Results show that the improved Otsu method make pointer instrument possible to obtained the accuracy binaryzation image even though the light is too bright or too dark , which improves the adaptability of pointer instrument to automatic recognize the light under different conditions. For the pressure gauges with range of 60MPa, the relative error identification reached to 0.005 when use the improved Hough Transform Algorithm.

Paper Details

Date Published: 8 October 2015
PDF: 10 pages
Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96752T (8 October 2015); doi: 10.1117/12.2202805
Show Author Affiliations
Li Xu, North South China Univ. of Water Resources and Electric Power (China)
Tian Fang, North South China Univ. of Water Resources and Electric Power (China)
Xiaoyu Gao, North South China Univ. of Water Resources and Electric Power (China)


Published in SPIE Proceedings Vol. 9675:
AOPC 2015: Image Processing and Analysis
Chunhua Shen; Weiping Yang; Honghai Liu, Editor(s)

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