基于双阈值级联分类器的金相图识别研究

Metallographic Image Recognition Based on Double Threshold Cascade Classifier

图像分类识别技术主要处理彩色或灰度图像,使用图像特征提取方法提取出同类样本的共性及不同样本的差异性,再配合恰当的分类器设计得到较高的分类识别率。选取200个锡基合金,通过二值化、形态学边缘提取和扫描线种子填充算法寻找封闭区域,用改进的多边形包含算法进行封闭区域包含判定,提取出金相组织的形状特征。对识别的金相组织设计双阈值级联分类器,根据特征识别进行归类。实验结果表明,所设计的分类器测试精度达到96%,识别速度快,能满足实际需求。

The processing object image classification technology mainly deals with color or grayscale images,using image feature extraction method to extract similar samples properly,similarities,differences in the different samples,the classification with the appropriate classifier design is finally able to get a higher rate;200 tin alloy were selected through the binarization.Morphological edge extraction and scan line seed fill algorithm were employed to find the closed areas which include judging by the improved algorithm of polygon.The shape features of the metallographic structure are extracted.A double threshold cascade classifier is designed to classify the recognition tissue according to the feature recognition.Experimental results show that the accuracy of the proposed classifier is 96%.The recognition speed is fast and the actual demand is met.