The Akazealgorithm is a typical image registration algorithm that has the advantage of high computational efficiency based on non-linear diffusion. However, it is weaker than the scale-invariant feature transformatio...
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The Akazealgorithm is a typical image registration algorithm that has the advantage of high computational efficiency based on non-linear diffusion. However, it is weaker than the scale-invariant feature transformation (SIFT) algorithm in terms of robustness and stability. We propose a new and improved version of the Akazealgorithm by using the SIFT descriptor based on sparse random projection (SRP). The proposed method not only retains the advantage of high efficiency of the Akazealgorithm in feature detection but also has the stability of the SIFT descriptor. Moreover, the computational complexity due to the high dimension of the SIFT descriptor, which limits the speed of feature matching, is drastically reduced by the SRP strategy. Experiments on several benchmark image datasets demonstrate that the proposed algorithm can significantly improve the stability of the Akazealgorithm, and the results suggest the better matching performance and robustness of the feature descriptor.
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