摘要
为了提高双目立体视觉算法正确匹配率,本文改进了特征点提取及立体匹配算法。特征点提取部分,定义了尺度方向不变角点;在匹配计算部分,利用极线约束计算特征点的候选匹配角点,采用视差梯度约束和最大向量角准则多约束条件实现精确匹配。对本文的改进算法进行了实验室以及水池试验验证:实验室中采集小盒图片;水池试验中,拖车带动船模在试验水池航行时,采集船后部的波浪图片。实验结果显示,改进算法在水池实验图片处理中的正确匹配率比尺度不变特征变换(SIFT)算法高14%。
This paper aims at improving the feature point extraction and stereo match algorithm to increase the right matching rate ol binocular stereo vision. For the feature point extraction, w e defined corners with a fixed scale and direction. To calculate the stereo match algorithm,a pole line constraint was used to match the corners. A disparity gradient constraint and maximum vector angle criterion were combined as a multi-constraint condition to determine a one- to-one exact match. A box image collected in the lab room and wave images generated by a ship model towed by a trailer in the experiment pool were used as objects to test the proposed algorithm. The re-sults show that the correct match rate ol the proposed algorithm is 14% higher than the scale invariant feature transform （SIFT） algorithm.
出处
《哈尔滨工程大学学报》
EI
CAS
CSCD
北大核心
2017年第3期465-470,共6页
Journal of Harbin Engineering University
基金
国家国际科技合作专项项目(KY10800150002)
国家自然科学基金项目(61371175)
关键词
双目视觉
SIFT算法
角点检测
测量
特征提取
匹配算法
binocular vision
scale invariant feature transform （SIFT）
corner detection
measurement
feature ex-traction
match algorithm
