在传统K-NN分类中,对于每个待测样本均需计算并寻找k个决策近邻,分类效率较低。针对该问题,提出一种双层结构的加速K-NN分类(K-NN classification based on double-layer structure,KNN_DL)方法。将正类和负类样本分别划分为多个不同子...在传统K-NN分类中,对于每个待测样本均需计算并寻找k个决策近邻,分类效率较低。针对该问题,提出一种双层结构的加速K-NN分类(K-NN classification based on double-layer structure,KNN_DL)方法。将正类和负类样本分别划分为多个不同子集,计算每个子集的中心和半径。当新样本进入时,选择k个决策近邻子集,若其具有相同的类别标签,将该样本标记为相应类别;反之,选择决策近邻子集中最近的k个决策近邻。这种双层结构的加速方式,压缩待测样本的决策近邻规模,提高效率。实验结果表明,KNN_DL方法能够获得较高的样本预测速度和较好的预测准确率。展开更多
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
传统的机器视觉采用二维RGB图像,难以满足三维视觉检测的要求,深度图像能直接反映物体表面的三维特征,正逐渐受到重视。该文提出的方案将RGB和深度信息相结合,分割出物体所在区域,并利用梯度方向直方图(HOG,histograms of oriented grad...传统的机器视觉采用二维RGB图像,难以满足三维视觉检测的要求,深度图像能直接反映物体表面的三维特征,正逐渐受到重视。该文提出的方案将RGB和深度信息相结合,分割出物体所在区域,并利用梯度方向直方图(HOG,histograms of oriented gradients)分别提取RGB图像和深度图像特征信息。在分类算法上,该文采用k最邻近节点算法(k-NN)对特征进行筛选,识别出目标物体。试验结果表明,综合利用深度信息和RGB信息,识别准确率很高,此方案能够对物体和手势进行很好识别。展开更多
文摘在传统K-NN分类中,对于每个待测样本均需计算并寻找k个决策近邻,分类效率较低。针对该问题,提出一种双层结构的加速K-NN分类(K-NN classification based on double-layer structure,KNN_DL)方法。将正类和负类样本分别划分为多个不同子集,计算每个子集的中心和半径。当新样本进入时,选择k个决策近邻子集,若其具有相同的类别标签,将该样本标记为相应类别;反之,选择决策近邻子集中最近的k个决策近邻。这种双层结构的加速方式,压缩待测样本的决策近邻规模,提高效率。实验结果表明,KNN_DL方法能够获得较高的样本预测速度和较好的预测准确率。
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
文摘传统的机器视觉采用二维RGB图像,难以满足三维视觉检测的要求,深度图像能直接反映物体表面的三维特征,正逐渐受到重视。该文提出的方案将RGB和深度信息相结合,分割出物体所在区域,并利用梯度方向直方图(HOG,histograms of oriented gradients)分别提取RGB图像和深度图像特征信息。在分类算法上,该文采用k最邻近节点算法(k-NN)对特征进行筛选,识别出目标物体。试验结果表明,综合利用深度信息和RGB信息,识别准确率很高,此方案能够对物体和手势进行很好识别。
