RESEARCH ARTICLE

Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status identification

  • Jian HUANG ,
  • Gui-xiong LIU
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  • School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510641, China

Received date: 11 Nov 2015

Accepted date: 23 Dec 2015

Published date: 31 Aug 2016

Copyright

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg

Abstract

The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm (k-NN) for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space (L*a*b*, hue saturation lightness (HSL), hue saturation value (HSV)) to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample Sr was classified by the k-NN algorithm with training set Tz according to the feature vector, which was formed from number of pixels, eccentricity ratio, compactness ratio, and Euler’s numbers. Last, while the classification confidence coefficient equaled k, made Sr as one sample of pre-training set Tz′. The training set Tz increased to Tz+1 by Tz′ if Tz′ was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples (a total of 21600 frames), the algorithm had a 98.65% identification accuracy, also selected five groups of samples to enlarge the training set from T0 to T5 by itself.

Cite this article

Jian HUANG , Gui-xiong LIU . Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status identification[J]. Frontiers of Mechanical Engineering, 2016 , 11(3) : 311 -315 . DOI: 10.1007/s11465-016-0376-z

Acknowledgements

The authors wish to acknowledge the financial support from the Science and Technology Plan Projects of Guangzhou, China (Grant No. 201504010037).
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