Point set registration method based on outer contour rough matching

Abstract

The invention discloses a point set registration method based on outer contour rough matching, which comprises the following steps: constructing an MVEE model for a template point set to be registered, and obtaining a minimum circumscribed ellipsoid parameter of the template point set, with the minimum circumscribed ellipsoid parameter at least comprising a long principal axis vector and a central point position of a constructed ellipse or ellipsoid; constructing an MVEE model for the target point set, and obtaining a minimum circumscribed ellipsoid parameter of the target point set; setting a plurality of direction templates in different directions; obtaining multiple groups of rigid transformation matrixes according to the direction template, the long principal axis vector and the center point position; performing rough matching on the template point set and the target point set by using the multiple groups of rigid transformation matrixes to obtain multiple groups of rough matching results; and carrying out fine matching on the multiple groups of rough matching results by utilizing a CPD algorithm, and selecting an optimal matching result. Global structure information of point sets is considered, rough matching is carried out on the two point sets through a minimum volume closed ellipsoid model, then precise registration is carried out through CPD, and a more precise registration result can be obtained.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to a point set registration method based on rough matching of outer contours. Background technique [0002] With the rapid development of image acquisition technology and the improvement of automation requirements, the analysis technology of multi-source, multi-view and multi-temporal images is becoming more and more urgent. As a technique to compare or fuse images under different conditions, image registration has been widely used in remote sensing data analysis, computer vision, and image processing. Through image registration technology, find and establish the corresponding relationship and transformation relationship between different images, so as to achieve the purpose of information fusion. [0003] Coherent Point Drift (CPD) is a robust image point set matching algorithm based on Gaussian mixture model, which is suitable for multi-dimensional point set...

AI Technical Summary

Problems solved by technology

However, these point sets to be registered do not necessarily move in the same direction, and this single motion consistency constraint will hinder the transformation of point sets in different directions

In the case where the corresponding relationship between the two point sets is the same, there will be serious errors in solving the deformation coefficient of the template point set

In addition, the transformation estimation in the process of point set registration is irreversible. If point sets in different directions, especially point sets with large rotation angles, are dragged by an external force in the same direction, this external force will continue to accumulate during the iterative process. , will eventually lead to different degrees of distortion, making the point set registration fail

[0004] In addition, the CPD algorithm uses the EM algorithm to iteratively update when solving the space transformation, so the initial matching parameters have a greater impact on the algorithm, and when the initial value is not selected properly, it often leads to a high time complexity

For point sets with degenerated rotation, unreasonable initial values ​​are likely to cause algorithm registration failure

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