摘要
在定位系统中,针对传统的SIFT(Scale Invariant Feature Transform)算法难以满足对墙角识别的实时性问题,提出了一种改进的特征提取与描述算法,称为双阈值FAST(Features from Accelerated Segment Test)特征检测的SIFT描述算法。该方法采用双阈值FAST进行特征提取,SIFT算法进行描述,可有效剔除大量非墙角特征点,大大提高了目标识别系统的速度。最后根据聚类与分类的思想,建立了视觉路标库,进行墙角的识别。实验结果表明,该算法在保证匹配正确率的同时提高了系统的实时性。
In the positioning system,the traditional SIFT（Scale Invariant Feature Transform） algorithm is difficult to meet real-time for the corner recognition problem,an improved feature extraction and description algorithm is put forward,which is called SIFT algorithm of double threshold FAST（Features from Accelerated Segment Test）. This method adopts double threshold FAST for feature extraction,SIFT algorithm for feature description which can effectively eliminate many non-corner feature points,greatly improve the recognition speed of the system. According to the thought of cluster and classification,we build a visual landmarks database for ceiling recognition. The experimental results show that the algorithm can guarantee the correct matching rate and improve the real-time performance of the system.
出处
《吉林大学学报（信息科学版）》
CAS
2017年第1期43-48,共6页
Journal of Jilin University（Information Science Edition）
基金
吉林大学"985"工程仿生科技创新平台基金资助项目
关键词
双目视觉
FAST-SIFT算法
聚类
路标库
binocular vision
features from accelerated segment test-scale invariant feature transform（FAST-SIFT） algorithm
cluster
landmark database
