Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status identification
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- 作者：Jian Huang ; Gui-xiong Liu
- 关键词：multi ; color space ; k ; nearest neighbor algorithm (k ; NN) ; self ; learning ; surge test
- 刊名：Frontiers of Mechanical Engineering
- 出版年：2016
- 出版时间：September 2016
- 年：2016
- 卷：11
- 期：3
- 页码：311-315
- 全文大小：380 KB
- 刊物类别：Engineering
- 刊物主题：Mechanical Engineering
Chinese Library of Science - 出版者：Higher Education Press, co-published with Springer-Verlag GmbH
- ISSN：2095-0241
- 卷排序：11
文摘
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.