摘要
针对物体多角度识别过程中存在着因偏转和俯仰角偏差而造成的匹配精度低等问题,本文提出了具有旋转不变性的SURF匹配优化思想.本思想采用聚类算法将提取的关键点进行分类,在类中通过距离高斯加权来得到关键点水平和垂直方向的Haar小波值,进而更精准地确定特征点主方向;针对匹配过程中出现的误匹配对,利用误匹配粗减思想进行剔除;之后,为进一步提高匹配机率,采用物体环视全景图作为后台基准图像.实验结果表明,本思想对物体多角度图像的识别机率和识别正确率明显提高,且匹配耗时也有所减低,并具有一定的实用性和推广性.
This paper proposes an optimized matching idea for a speeded up robust features(SURF)algorithm with a rotation invariance that can solve the problems associated with low matching accuracy,which are caused by rotation and pitch angle deviation in the multi-angle recognition of an object.In this paper,we utilize the clustering method to classify the extracted key points.The key points are then gained on the horizontal and vertical Haar wavelet values by distance Gaussian weights to more accurately determine the main direction of the feature points.Simultaneously,aiming at the mismatching pairs appearing in the matching process,we remove them by introducing the wrong match rough reduction method.In addition,to further improve the matching opportunities,we view the panoramic image of the object as the background reference image.Experimental results show that the algorithm has a greater probability and recognition accuracy with regard to multi-angle image recognition of objects and consumes less time.It also has a certain degree of practicality and generalization.
作者
张玉珍
侯守明
连盼盼
杜成菲
ZHANG Yuzhen;HOU Shouming;LIAN Panpan;DU Chengfei(School of Computer Science and Technology,Henan Polytechnic University,Jiaozuo 454000,China)
出处
《信息与控制》
CSCD
北大核心
2019年第1期42-49,共8页
Information and Control
基金
河南省科技攻关计划项目(172102210273
182102210086)
关键词
多角度识别
主方向重建
误匹配剔除
环视全景图
multi-angle recognition
the reconstruction of main direction
mismatch removed
around panoramic image
