摘要
考虑到随机测量矩阵存在硬件上存在无法实现的缺陷,结合压缩感知的稀疏投影理论,提出了基于多项式确定性矩阵的尺度不变特征变换(SIFT)医学图像配准算法。通过增加方向梯度数提高特征向量的有效性,利用测量数为7的多项式确定性矩阵对关键点特征向量进行降维,用欧式距离作为特征向量匹配的相似性度量,kd数据结构避免穷举。实验结果表明,该算法和传统SIFT算法及几种改进的SIFT算法相比,配准性能有了显著提高,同时确定性矩阵有利于图像配准系统的硬件实现。
Given that random measurement matrix has defect in hardware realization, a scale-invariant feature transform （SIFT） based on polynomial deterministic matrix algorithm is proposed combining with the sparse proiection of compressive sensing theory. The effectiveness of feature vector is enhanced by increasing the numbers of orientation gradient. The dimension of SIFT feature vector is decreased by a polynomial deterministic matrix with the measurement numbers of 7. Accordingly, the Euclidean distance is introduced to compute the similarity and dissimilarity between feature vectors used for image registration, and kd data structure is used to avoid exhaustion. Experimental results show that the proposed algorithm has better performance than the traditional SIFT algorithm and some current modified SIFT algorithms. At the same time, the deterministic matrix is beneficial to hardware implementation of image registration system.
出处
《激光与光电子学进展》
CSCD
北大核心
2016年第8期122-128,共7页
Laser & Optoelectronics Progress
基金
国家自然科学基金(11547212)
关键词
图像处理
图像配准
压缩感知
特征提取
多项式确定性矩阵
image processing
image registration
compressive sensing
feature extraction
polynomialdeterministic matrix
