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 prop...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 T~ according to the feature vector, which was formed from number ofpixels, eccentricity ratio, compact- ness ratio, and Euler＇s numbers. Last, while the classification confidence coefficient equaled k, made Sr as one sample ofpre-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 To to T5 by itself. Keywords multi-color space, k-nearest neighbor algorithm （k-NN）, self-learning, surge test展开更多
In an automatic bobbin management system that simultaneously detects bobbin color and residual yarn,a composite texture segmentation and recognition operation based on an odd partial Gabor filter and multi-color space...In an automatic bobbin management system that simultaneously detects bobbin color and residual yarn,a composite texture segmentation and recognition operation based on an odd partial Gabor filter and multi-color space hierarchical clustering are proposed.Firstly,the parameter-optimized odd partial Gabor filter is used to distinguish bobbin and yarn texture,to explore Garbor parameters for yarn bobbins,and to accurately discriminate frequency characteristics of yarns and texture.Secondly,multi-color clustering segmentation using color spaces such as red,green,blue(RGB)and CIELUV(LUV)solves the problems of over-segmentation and segmentation errors,which are caused by the difficulty of accurately representing the complex and variable color information of yarns in a single-color space and the low contrast between the target and background.Finally,the segmented bobbin is combined with the odd partial Gabor’s edge recognition operator to further distinguish bobbin texture from yarn texture and locate the position and size of the residual yarn.Experimental results show that the method is robust in identifying complex texture,damaged and dyed bobbins,and multi-color yarns.Residual yarn identification can distinguish texture features and residual yarns well and it can be transferred to the detection and differentiation of complex texture,which is significantly better than traditional methods.展开更多
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 T~ according to the feature vector, which was formed from number ofpixels, eccentricity ratio, compact- ness ratio, and Euler＇s numbers. Last, while the classification confidence coefficient equaled k, made Sr as one sample ofpre-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 To to T5 by itself. Keywords multi-color space, k-nearest neighbor algorithm （k-NN）, self-learning, surge test
基金Key Research and Development Plan of Shaanxi Province,China(No.2023-YBGY-330)。
文摘In an automatic bobbin management system that simultaneously detects bobbin color and residual yarn,a composite texture segmentation and recognition operation based on an odd partial Gabor filter and multi-color space hierarchical clustering are proposed.Firstly,the parameter-optimized odd partial Gabor filter is used to distinguish bobbin and yarn texture,to explore Garbor parameters for yarn bobbins,and to accurately discriminate frequency characteristics of yarns and texture.Secondly,multi-color clustering segmentation using color spaces such as red,green,blue(RGB)and CIELUV(LUV)solves the problems of over-segmentation and segmentation errors,which are caused by the difficulty of accurately representing the complex and variable color information of yarns in a single-color space and the low contrast between the target and background.Finally,the segmented bobbin is combined with the odd partial Gabor’s edge recognition operator to further distinguish bobbin texture from yarn texture and locate the position and size of the residual yarn.Experimental results show that the method is robust in identifying complex texture,damaged and dyed bobbins,and multi-color yarns.Residual yarn identification can distinguish texture features and residual yarns well and it can be transferred to the detection and differentiation of complex texture,which is significantly better than traditional methods.
