摘要
将传统相似三角形匹配方法和快速二维聚类匹配方法进行融合,再利用基于灰度的方法对部分伪匹配三角形进行剔除,实现了一种新的抗旋转、缩放的特征点匹配算法.融合后的算法对有效点的要求降低,同时通过在复数向量空间中进行相似三角形检索及参数聚类,提高了算法的效率.
A new feature points matching algorithm based on similar triangles and 2-D parameters clustering was proposed, which has the function of anti-rotation and scaling. A part of pseudo-match triangles was eliminated by using redefined MCD distance approach, which greatly reduced the demand on efficient points. Meanwhile, plural vector space was used in similar triangle searching and parameters clustering, which greatly decreased the cost of this algorithm.
出处
《湘南学院学报》
2009年第5期72-75,81,共5页
Journal of Xiangnan University
基金
国家自然科学基金资助项目(60573079)
关键词
特征点匹配
相似三角形检索
二维聚类
伪匹配三角形剔除
feature points matching
similar triangles retrieval
2-D parameters clustering
pseudo-match triangular eliminate
