Sub-pixel characteristic point detection-based image matching method

Abstract

The invention discloses a sub-pixel characteristic point detection-based image matching method. The method comprises the following steps of: performing down-sampling on an image to be detected until the smaller one of the length and the width of the image to be detected is less than 8 pixels, acquiring and performing geometric progression variance Gaussian filtering on each down-sampled image to obtain continuous and progressive Gaussian blur images of which the maximum value of the variance is 2, establishing and acquiring the Gaussian pyramid of the image to be detected, evaluating a second derivative Ixx in x direction, a second derivative Iyy in y direction and a second derivative Ixy in xy direction; calculating the Hessian matrix of the Gaussian pyramid of the image to be detected and all local maximum value points of a Harris function value; and fitting a three-dimensional quadratic function with each local maximum value point on the Harris function value and 26 neighborhood points around each local maximum value point, updating the position of an extreme point of the quadratic function by using the floating number position of the extreme point, performing sub-pixel accuracy positioning modification on the position of the extreme point and outputting all characteristic points.

Description

technical field [0001] The invention belongs to the field of pattern recognition, relates to technologies such as image processing and computer vision, and particularly relates to feature detection, object recognition, target tracking and the like. Background technique [0002] Image feature detection is a basic problem in computer vision, and a stable and efficient image feature detection algorithm provides a solid underlying foundation for solving other problems. With the development of technology and the gradual reduction of hardware equipment prices, cameras and video cameras have become one of the widely used equipment in people's daily life. People's requirements for scene perception have changed from the original two-dimensional perception to three-dimensional perception, that is, to perceive the three-dimensional shape of objects in the real world and the three-dimensional posture of the scene. Due to the variety of objects in the real world, direct 3D modeling of a...

AI Technical Summary

Problems solved by technology

However, for computers, the detection of feature points in images is a very difficult problem

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