应用小波包能量谱及SVM的安瓿内浮类异物识别

目的：为了解决安瓿内漂浮物与悬浮物的识别问题，本文提出了一种基于小波包能量谱的特征提取及支持向量机的识别方法。方法：首先通过图像序列差分及点检测分割提取杂质存在区图像作为目标区；然后将目标区沿安瓿瓶轴线方向逐行叠加形成一维信号；对一维信号进行小波包分解，采用主成分分析方法提取小波包分解特征向量中独立主成分；以小波包特征向量中独立主成分的能量谱作为异物类型特征；将提取的特征作为支持向量机的输入向量，采用序列最小优化方法实现训练样本快速分类。实验过程中选择不同类型的核函数和相应参数进行训练和测试。结果：实验结果显示，相对于传统BP网络，SVM在将识别用时减少近60%，识别精度提高了35%。结论：能够满足在生产中对浮类杂质的提取和快速识别的要求。

Recognition of floating particles in ampoules by wavelet packet energy spectrum and SVM

A method based on the feature extraction of a wavelet packet energy spectrum and the recognition of a Support Vector Machine(SVM) was presented to solve the problem of recognizing the floating and suspending impurities in ampoules. Firstly, an impurity zone’s image was extracted as an object image through the image sequence difference and point detection division. Then, a 1D signal could be obtained through adding the ROI row by row in the axis direction of an ampoule. The 1D signal was decomposed by a wavelet packet, the independent primary components in the wavelet packet feature vector were extracted by using Primary Component Analysis(PCA), and the wavelet packet energy spectrum of the independent primary components was taken as the feature of impurity types.Furthermore,the extracted feature was taken as the input vector of a SVM,and the sample features could be classified rapidly by a sequential minimal optimization method through training. Different types of core functions and corresponding parameters were selected for training and testing in the experiments,and obtained results show that the recognition period of SVM has decreased by 60% and the recognition precision improved by 35%,respectively, as compared with those of the BP network. This method can meet the requirements of the floating particles for feature extraction and rapid recognition in production.