摘要
针对传统的特征向量计算方法复杂度高、耗时长、占用内存多等缺点,提出了一种基于字典描述向量的图像配准方法。该算法采用K-奇异值分解(K-SVD)方法生成字典,通过比较特征点临近区域图像与字典中基底图像的相似性得到特征描述向量,从而降低了描述向量的计算复杂度,提高了算法的实时性。实施该算法时,首先通过随机KD树算法对参考图像和待配准图像的特征点进行匹配,然后使用经典随机抽样一致性(RANSAC)算法剔除误匹配点对,最后应用最小二乘法对得到的匹配点对进行参数估计,从而得到两幅待配准图像的空间几何变换关系。实验表明结果,本文提出的描述向量计算方法降低了描述向量的存储空间,加快了特征匹配的速度,可在保证配准准确度的前提下实现配准过程。
As traditional description vector calculation method used in image registration is too complex, time consuming and taking up more memory, a novel dictionary based local feature description algorithm was proposed. The K-singular Value Decomposition（ KSVD ） method was used to generate dictionary and the feature descriptor was obtained by comparing the similarity between feature point region in images and elements in the dictionary. By above, the description vector generation algorithm was simplified and a higher feature matching speed was obtained. The matching process could be car-ried out by using randomized KD（k-dimension）tree algorithm. Then , the Random Sample Consensus （RANSAC） was used to choose the correct matching pairs. Finally, the transform parameters were estimated by using the least square method and the space geometric transformation of two images to be registrated was obtained. Results from experiments show that the proposed method reduces the description vector storage space, speeds up the feature matching and implements the registration process in real time.
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2014年第6期1613-1621,共9页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.61172111)
关键词
字典
特征描述向量
图像配准
K-奇异值分解算法
dictionary
feature description vector
image registration
K-singular Value Decomposition（KSVD） method
