Acta Optica Sinica, Volume. 34, Issue 11, 1110001(2014)
Image Registration Algorithm Based on Sparse Random Projection and Scale-Invariant Feature Transform
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.
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Yang Sa, Yang Chunling. Image Registration Algorithm Based on Sparse Random Projection and Scale-Invariant Feature Transform[J]. Acta Optica Sinica, 2014, 34(11): 1110001
Category: Image Processing
Received: Apr. 22, 2014
Accepted: --
Published Online: Oct. 13, 2014
The Author Email: Sa Yang (yangsa@gdei.edu.cn)