Improved SIFT image registration algorithm on characteristic statistical distributions and consistency constraint

In the process of SIFT (Scale Invariant Feature Transform) image registration algorithm; the principal orientation is affected by the dispersion of histogram. Besides; the feature descriptor section of conventional sift does not make full use of local feature information. As to these problems; an improved sift algorithm on characteristic statistical Distributions and consistency constraint will be presented in this paper. Firstly; DoG scale space feature point detection method is adopted to extract key points. Then; in the process of principal orientation generation; our method selects line with maximum dispersion. Furthermore; this method generates feature descriptor based on characteristic statistical Distributions in polar coordinate. Finally; a new matching method based on consistency constraint will be introduced. In experiments; we test the performances of our propose method. The experimental results demonstrate the feasibility and effectiveness of our approach.