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ORB feature point matching method with scale invariance

A technology of feature point matching and scale invariance, applied in the field of image processing, can solve problems such as difficult application of real-time performance and slow operation speed

Inactive Publication Date: 2015-08-19
FUZHOU UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

SURF has good scale invariance, but the operation speed is slow, so it is difficult to apply in systems with high real-time requirements

Method used

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  • ORB feature point matching method with scale invariance
  • ORB feature point matching method with scale invariance
  • ORB feature point matching method with scale invariance

Examples

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Embodiment Construction

[0023] Attached below picture And embodiment further illustrate the present invention.

[0024] This embodiment provides an ORB feature point matching method with scale invariance, such as picture 1, including the following steps:

[0025] Step S1: Input to be detected picture like, yes picture Like performing an improved SURF feature point detection and determining the feature point coordinates;

[0026] Step S2: For the above step S1 picture like build picture like a pyramid;

[0027] Step S3: remove close picture Feature points like edges;

[0028] Step S4: Calculate the centroid directions of the remaining feature points;

[0029] Step S5: calculate the ORB feature point descriptor;

[0030] Step S6: using the K nearest neighbor algorithm for feature point matching;

[0031] Step S7: Filter the matching pairs of feature points and output the detected picture picture.

[0032] In this embodiment, the improved SURF feature point detection in step S1 ...

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Abstract

The present invention relates to an ORB feature point matching method with scale invariance. The method is characterized by comprising the steps of a step S1 of inputting an image to be detected, performing improved SURF feature point detection on the image, and determining coordinates of the feature points; a step S2 of establishing an image pyramid for the image in the step S1; a step S3 of removing the feature points close to edges of the image; a step S4 of calculating directions of centers of mass of the remained feature points; a step S5 of calculating ORB feature point descriptors; a step S6 of adopting a K-nearest neighbor algorithm to carry out feature point matching; and a step S7 of screening feature point matching pairs and outputting the detected image. According to the method provided by the present invention, the SURF with the scale invariance and the ORB are combined, the image pyramid is introduced, and an ORB feature point matching algorithm is improved, so as to enable the method to have the scale invariance and maintain the characteristic of fastness of the ORB algorithm.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to an ORB feature point matching method with scale invariance. Background technique [0002] The detection, description and matching of image feature points is an extremely important branch in the field of image processing. The realization of many technologies such as image recognition, video tracking, image stitching, and 3D reconstruction depends on the detection, description, and matching of image feature points. [0003] ORB is an excellent feature point detection, description and matching algorithm. It uses fast FAST corner detection and binary-coded Brief descriptor. It has fast operation speed, good illumination robustness, and good affine performance, but it does not have Scale invariance causes ORB to have limitations in the application field. SURF is a robust image recognition and description algorithm. It uses integral image and box filter for convolution, uses Gaussian ...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06T7/60
Inventor 姚剑敏郭太良叶芸张永爱林志贤周雄图
Owner FUZHOU UNIV
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