Rapid SIFT extraction method based on information quantity

Abstract

The invention discloses a rapid SIFT extraction method based on information quantity. The method comprises the steps that one-time Gaussian convolution is carried out on the basis of an original drawing to obtain a Gaussian convolution drawing; the eight-point neighborhood extremum method is adopted on the original drawing and the Gaussian convolution drawing to obtain candidate feature points, vertical projection of each candidate feature point is carried out on the original drawing, the circular region with the projection point on the original drawing as the center and four pixels as the radius is searched for the candidate feature point, closest to the projection point, on the original drawing, and the found candidate feature point is used as the feature point of the unchanged size; unit radius information quantity obtained by dividing the circular regions with the radiuses ranging from one pixel to twenty pixels by the radius is calculated, and the radius obtained when the information quantity is the maximum value is used as the local size; a twelve-dimensional SSIFT feature vector is calculated, the vector is normalized, and rapid matching among images is achieved. The rapid SIFT extraction method has the advantages that the calculation time is shortened, the real-time performance is improved, and target matching can be carried out on the noisy complex environment.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a fast SIFT extraction method based on information volume. Background technique [0002] In recent years, the multi-scale space theory of images has gradually become a research hotspot. In 1999 and 2004, Canadian scientist Lowe proposed a stable scale-invariant feature extraction algorithm SIFT on the basis of his predecessors, which became a milestone in the field of vision. However, SIFT has the disadvantages of large amount of calculation and incomplete affine. Therefore, domestic and foreign scholars have proposed many improved algorithms, such as the simplified algorithm SSIFT proposed by Liu Li et al., the PCA-SIFT algorithm proposed by Ke, and the SURF (speeded up Robust features) algorithms reduce the time complexity of the SIFT operator to a certain extent, but correspondingly lose the matching performance of the algorithm. Mortensen adds global texture features ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a fast SIFT extraction method based on the amount of information, which solves the problems t

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