摘要
为了提高加速鲁棒特征(SURF)算法的实时性和准确性,本文提出了一种结合AGAST角点检测和改进的SURF特征描绘算法。首先利用AGAST角点检测模板检测特征点,再使用增加对角信息的哈尔小波响应来生成特征点的描述子,之后利用特征袋对产生的描述子进行编码并生成新的特征向量,最后利用支持向量机(SVM)对特征向量进行分类,完成识别。本文以SIFT和SURF算法为对照,分别进行不同视角、光照和尺度的识别实验。实验结果表明,本文算法的平均识别率为98.0%、96.9%、97.1%,平均时间分别为66.1 ms、79.3 ms、41.0 ms,在识别率上较优于SURF算法,所耗时间约是SURF算法的1/3。
To improve the real-time performance and the accuracy of the SURF algorithm,an algorithm combined 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 Vector 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.
出处
《中国光学》
EI
CAS
CSCD
2017年第6期719-725,共7页
Chinese Optics
基金
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)~~
