摘要
介绍了基于计算机视觉的储粮活虫检测系统软件部分各环节的具体实现。系统运用基于标记点透视变换的图像配准方法,对近红外图像进行倾斜、变形等校正;采用基于双区域连通阈值面积比的区域生长法判别出近红外图像中的活虫;融合多源图像的信息,准确定位出可见光图像中的活虫。提取出活虫的21个整体形态学特征和7个局部形态学特征,把特征空间优化为7维,运用SAA-SVM分类器进行识别分类。结果表明,检测系统对15类活虫的正确识别率达到94.8%。
The detection system for stored-grain live insects was introduced based on visible-near infrared computer vision, and the software realization of the main parts in the system was given. The near infrared images were sloped and distorted by the image registration based on projection transformation with markers. The region-growing method for identifying the live insects in the near infrared image was proposed based on the area ratio of two thresholds for connecting regions. The live insects in the visible image were located accurately by the information fusion of the multi-source images. The live insects in the visible image were located accurately by the information fusion of the multi-source images. Twenty-one global morphological features and seven local morphological features of the live insects were extracted, and the feature space was optimized to seven dimensions. The insects were classified by the classifier based on simulated annealing algorithm and support vector machine. The results showed that the recognition accuracy was 94. 8% for the fifteen species of the live insects.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2012年第8期180-186,共7页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金资助项目(31101085
30871449)
河南省基础与前沿研究计划资助项目(122300410145)
河南省高等学校青年骨干教师资助项目(2011GGJS-094)
河南省教育厅自然科学研究计划资助项目(2011B210028)
华北水利水电学院高层次人才科研启动项目
关键词
储粮活虫
计算机视觉
特征提取
图像识别
检测
Stored-grain live insects, Computer vision, Feature extraction, Image recognition, Detection
