中国光学2017，Vol.10Issue(6)：719-725,7.DOI:10.3788/CO.20171006.0719
基于改进的加速鲁棒特征的目标识别
Object detection based on improved speeded-up robust features
摘要
Abstract
To improve the real-time performance and the accuracy of the SURF algorithm , an algorithm com-bined with AGAST corner detector and improved SURF feature descriptor is proposed .Firstly, feature points are detected by using AGAST corner detection template .Secondly , the Haar wavelet response with increased diagonal information is used to generate descriptor of feature points .Then, the generated descriptor is encoded by the feature bag and a new feature vector is generated .At last, the classification is fulfilled by Support Vec-tor Machine(SVM).Finally, SVM is used to classify the feature vectors to complete the detection .Detection experiments for different view-points, illumination and scales are conducted respectively using SIFT and SURF algorithm as a control .The results show that the average detection rate of this algorithm is 98.0%, 96.9%and 97.1%, and the average time is 66.1 ms, 79.3 ms and 41.0 ms, respectively , which is better than that of SURF algorithm , and the time consumption is about 1/3 of the SURF algorithm .关键词
图像处理/目标识别/加速鲁棒特征/AGAST角点检测Key words
image processing/object detection/Speeded-Up Robust Features/AGAST corner detection分类
信息技术与安全科学引用本文复制引用
龙思源,张葆,宋策,孙保基..基于改进的加速鲁棒特征的目标识别[J].中国光学,2017,10(6):719-725,7.基金项目
中国科学院长春光学精密机械与物理研究所重大创新资助项目(No.Y3CX1SS14C) Supported by Major Innovation Project of CIOMP , CAS, China(No.Y3CX1SS14C) （No.Y3CX1SS14C）
