Nonlinear scale space-based ORB feature point matching method

A feature point matching and scale space technology, applied in the field of image processing, can solve problems such as short iterative convergence steps, loss of details, blurred boundaries, etc., to achieve the effect of overcoming blurred boundaries and loss of details, and fast operation speed

Inactive Publication Date: 2018-04-20
ANHUI UNIVERSITY OF TECHNOLOGY AND SCIENCE
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AI Technical Summary

Problems solved by technology

[0003] However, the ORB algorithm does not solve the problem of scale invariance well. Although the existing improved ORB algorithm introduces a linear Gaussian pyramid to construct the scale space, it is easy to cause blurred boundaries and loss of details.
This problem can be solved by constructing a nonlinear scale space, but when the traditional method is based on the forward Euler method to solve the nonlinear diffusion equation, the iterative convergence step size is too short, which takes a long time and has high computational complexity

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  • Nonlinear scale space-based ORB feature point matching method
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  • Nonlinear scale space-based ORB feature point matching method

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Embodiment Construction

[0043] The invention is an ORB feature point matching method in a nonlinear scale space, which combines KAZE to improve the ORB, introduces a nonlinear scale space, and uses the AOS algorithm to solve all images in the nonlinear scale space, so that it has scale invariance And it retains the characteristics of fast ORB calculation speed, and also overcomes the problem that the existing improved algorithm uses linear Gaussian pyramid to construct the scale space, which is easy to cause blurred boundaries and loss of details.

[0044] Specific as figure 1 shown, including the following six steps

[0045] Step 1: Input an image, and use the idea of ​​KAZE algorithm to construct a nonlinear scale space; use AOS algorithm and variable conduction-diffusion method to construct a nonlinear scale space by referring to KAZE algorithm.

[0046] The process of nonlinear scale space construction is to firstly perform Gaussian filtering on the input original image, then calculate the gradi...

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Abstract

The invention discloses a nonlinear scale space-based ORB feature point matching method. The method comprises the following steps of 1, inputting an image and constructing a nonlinear scale space; 2,performing feature point detection by utilizing FAST; 3, calculating centroid directions of residual feature points; 4, calculating ORB feature point descriptors; 5, performing feature point matchingby adopting a BruteForce algorithm; and 6, screening feature point matching pairs and outputting a detected image. The method has the advantages that ORB has scale invariance, the characteristic of high calculation speed of the ORB is reserved, and the problems of boundary fuzziness and detail loss easily caused by constructing a scale space by utilizing a linear Gaussian pyramid in an existing improved algorithm are solved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an ORB feature point matching method based on a nonlinear scale space. Background technique [0002] The ORB feature is currently the most representative real-time image feature. It improves the problem that the FAST detector does not have directionality, and uses the improved binary descriptor BRIEF with extremely fast speed to make it invariant to rotation, so that the entire The link of image feature extraction is greatly accelerated. The real-time performance of the ORB algorithm is much higher than that of the classic SIFT and SURF algorithms. [0003] However, the ORB algorithm does not solve the problem of scale invariance well. Although the existing improved ORB algorithm introduces a linear Gaussian pyramid to construct the scale space, it is easy to cause blurred boundaries and loss of details. This problem can be solved by constructing a nonlinear scale space. However...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46
CPCG06V10/462G06F18/22
Inventor 许钢张阳江娟娟邢广鑫袁悦张星宇
Owner ANHUI UNIVERSITY OF TECHNOLOGY AND SCIENCE
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