Characteristic point matching method based on relativity measurement

Abstract

The invention relates to a feature point matching method based on correlation measurement, which comprises: shooting multiple scene images to be matched and inputting the scene images into a computer; calculating gradient of each pixel of the images, and extracting feature point information of the images; for each extracted feature point, dividing a round neighborhood into blocks by taking the feature point as a center, and calculating a gradient mean of each subarea obtained by the dividing step; utilizing the gradient of each pixel point of the subarea and the gradient mean of the subarea to establish a Harris correlation matrix of the subarea and calculate a determinant and a track of the Harris correlation matrix; utilizing the determinant and the track of the Harris correlation matrix to construct a Harris correlation measurement, and utilizing the Harris correlation measurement to construct a Harris correlation descriptor; and calculating Euclidean distance among the descriptors of the feature points, and applying measurement criteria to carry out matching. The method does not need to calibrate parameters of the pickup camera, does not need human participation during matching, automatically finishes matching, and has the characteristics of simplicity, practicality, dense matching points, high matching precision, good robustness and the like.

Description

technical field [0001] The invention belongs to the technical field of computer-aided automatic image processing, and in particular relates to a matching technology of image features. Background technique [0002] In our daily life and many fields, it is essential to process electronic images, especially to extract and retrieve specific information in images, such as image retrieval, object segmentation and recognition, 3D reconstruction, and augmented reality. It involves the matching of image features, especially the matching of feature points. The traditional method is based on the gray value of image pixels. Although this method is simple and easy to implement, the matching effect is not ideal due to the insufficient information used. [0003] In recent years, with the development and application of computer vision, people have begun to explore new methods, among which there are more typical methods based on image gradients and methods based on image differentiation. I...

AI Technical Summary

Problems solved by technology

In the method based on the image gradient, the matching performance of the relatively simple descriptor is not good, and the time complexity and space complexity of the descriptor calculation with the better matching performance are relatively high; the descriptor based on the image differentiation is more complex in structure, And the outstanding shortcoming is that it is not robust to image noise, and real images often have noise, so it is difficult to satisfy

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