摘要
尺度不变特征变换(SIFT)算法是图像配准中一种用来描述局部特征最稳健,使用最广泛的方法。针对存在关键点特征描述向量维数较高,算法计算复杂的问题,提出了一种基于稀疏随机投影(SRP)与SIFT相结合的图像配准算法,该算法把压缩感知理论的稀疏特征表示概念引入SIFT算法中,即SRP-SIFT,用稀疏特征表示方法对SIFT关键点特征向量进行提取,再使用相应的L1距离度量进行特征向量的匹配。对新算法和相关SIFT算法进行了图像配准实验,实验结果表明,SRP-SIFT算法对包含复杂结构内容的图像配准性能优于传统SIFT算法,配准效率与几种改进的SIFT算法相当,但运算速度比传统SIFT算法和几种改进的SIFT算法有明显提高。
Scale-invariant feature transform （SIFT） is one of the most robust and widely used local feature descriptor for image registration, however, the computational complexity of its key point descriptor computing stage is quite expensive and also the dimensionality of the key point feature vectors is relatively high. For speeding up the SIFT computation, a novel sparse random projection （SRP） based algorithm, namely SRP-SIFT, is proposed by combining SIFT with sparse feature representation methods from the compressive sensing theory. Accordingly, L1 norm is introduced to compute the similarity and dissimilarity between feature vectors used for image registration. The experimental results show that the proposed SRP-SIFT algorithm is much faster than the standard SIFT algorithm while the performance is favorably comparable when performing complex structured scene image registration applications.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2014年第11期98-102,共5页
Acta Optica Sinica
基金
国家自然科学基金(60972135)
关键词
图像处理
尺度不变特征变换
压缩感知
特征提取
稀疏随机投影
image processing
scale-invariant feature transform
compressive sensing
feature extraction
sparserandom projection
