摘要
提出了一种新的基于多级SVM分类器的设计方法,并将该方法应用于回转窑的正常烧结、过烧结和欠烧结3种烧结状态的识别.即首先利用改进的双快速行进法从烧结图像中分割出物料区、黑把子区、火焰区、充分燃烧区等关心区域(ROIs),然后从ROIs中提取颜色、形状与纹理等特征,基于One-Versus-Another方法建立烧结状态预处理分类器模型,进行烧结状态的多类别分类;其次研究预处理分类器出错样本点的分布,将容易混淆的样本点作为一类;最后基于多级SVM方法重新建立分类器模型,进行烧结状态的分类.实验证明该方法快速、准确、推广性强.
A new design method based on multilevel SVM classifier is presented to recognize the normal sintering, oversintering and undersintering states in rotary kiln. The ROIs（regions of interesting） were segmented from the images of sintered materials by an improved dual-fast marching method, including the zones of materials, blackbar, flame and full calcination. Then, the characteristics of colour, shape and vein were extracted from ROIs to develop a pre-treated classifier model based on One-Versus-Another method to classify the different sintering states. The distribution of wrong sample points provided by the pre-treated classifier was studied, and the sample points that are easy to confuse with each other were classified as the same class. Based on the multilevel SVM, a new classifier model was thus redeveloped for different sintering states. The experimental results showed that the new model is fast and accurate with wide applications.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第1期54-57,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(60534010)
国家重点基础研究发展规划项目(2002CB312201)
国家高技术研究发展计划重点项目(2007AA041404)
关键词
烧结状态
SVM
模式识别
回转窑
sintering state
SVM（support vector machine）
pattern recognition
rotary kiln
