Feature vector-based fast and high-precision robustness matching method

A technology of eigenvectors and matching methods, applied in image data processing, instruments, character and pattern recognition, etc., can solve problems such as low feature matching accuracy, lack of matching algorithms, surface distortion, etc., to ensure accuracy and robustness , high robustness, fast matching process

Inactive Publication Date: 2011-08-31
BEIHANG UNIV
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AI Technical Summary

Problems solved by technology

[0007] There are certain problems in the feature-based matching method: first, the accuracy of feature matching is not high; second, feature extraction based on single image may not be able to extract features with the same name at the same time, even if they are extract

Method used

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  • Feature vector-based fast and high-precision robustness matching method

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Embodiment Construction

[0022] Step 1: Use the SIFT (Scale Invariant Feature Transform) feature extraction algorithm to extract feature points from the image to be matched and calculate the SIFT feature vector.

[0023] Step 1.1 For an input image I(x, y), build an image pyramid, and then use Gaussian filtering to perform convolution operation on each level of image. The convolution formula is as follows:

[0024] L ( x , y , kδ ) = G ( x , y , kδ ) ⊗ I ( x , y )

[0025] where G(x, y, kδ) is the standard Gaussian equation, where kδ represents the standard deviation dimension, and L(x, y, kδ) is the filtered image. First, according to different k values, k 1 , k 2 ,...,k n , generating a series of corr...

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Abstract

The invention discloses a feature vector-based fast and high-precision robustness matching method. Due to the changes of the environment, the influences of the target motion and the defects of sensors, the shot images are not only influenced by the noise but also have severe grayscale distortion and geometric distortion, therefore, how to achieve and realize high precision, high matching accuracy rate, fast speed, strong robustness and strong anti-interference performance becomes a goal pursued by the matching method. The invention discloses a sub-pixel-level fast and high-precision robustness matching, comprising the steps of: respectively extracting the SIFT (Scale Invariant Feature Transform) feature vectors of two pictures to be processed; carrying out PCA (Principal Components Analysis) dimension reduction treatment on the two pictures; matching the two feature vectors by utilizing Kd-tree; then screening the obtained matching points by utilizing an RANSAC algorithm; and causing the matching to achieve the sub-pixel level through a surface fitting technology so as to obtain the feature point pair of the high-precision robustness. Furthermore, the matching speed is increased by using an even point getting method. Through the invention, the obtained image matching result has extremely high matching precision and faster running speed and the experiment effect is excellent.

Description

technical field [0001] The invention relates to image feature point matching, in particular to a fast, high-precision and robust matching method based on feature vectors, which belongs to the field of digital image processing. Background technique [0002] Due to the change of the environment, the influence of target motion and the defect of the sensor, the captured image is not only affected by noise, but also has serious grayscale distortion and geometric distortion. Therefore, how to achieve high precision, high matching accuracy, fast speed, robustness and anti-interference, and parallel realization of the matching method has become the pursuit of the goal. [0003] According to the level of feature extraction, image matching can be generally divided into three categories: grayscale-based image matching, feature-based image matching, and image matching based on understanding and interpretation. The grayscale-based matching method has simple ideas and is easy to implemen...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/46G06K9/64
Inventor 百晓赵猛
Owner BEIHANG UNIV
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