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An image registration method and apparatus based on an improved SIFT and hash algorithm

A technology of image registration and hash algorithm, which is applied in image analysis, image data processing, calculation, etc., can solve the problems of increasing invalid data in sample data sets, iteratively finding the optimal parameter matrix for matching point data sets, etc., and achieves reduction The effect of invalid data, reducing computational complexity, and improving matching accuracy

Inactive Publication Date: 2019-03-01
CHINA UNIV OF MINING & TECH
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Problems solved by technology

[0005] At present, when most of the images are registered using SIFT and its improved algorithm, there are some mismatched pairs in the obtained matching point pairs, which increases the invalid data in the sample data set, making iteratively searching for the optimal parameter matrix in the matching point data set. difficult

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  • An image registration method and apparatus based on an improved SIFT and hash algorithm
  • An image registration method and apparatus based on an improved SIFT and hash algorithm
  • An image registration method and apparatus based on an improved SIFT and hash algorithm

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[0069] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0070] An embodiment of the present invention provides an image registration method based on an improved SIFT and hash algorithm, which will be described below with reference to the accompanying drawings.

[0071] refer to figure 1 As shown, the method includes: steps S101-S106;

[0072] S101. Construct Gaussian scale spaces for at least two images to be matched respectively;

[0073] S102. Detecting multiple local extremum point...

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Abstract

The invention relates to an image registration method and a device based on an improved SIFT and a hash algorithm. The method comprises the following steps: at least two images to be matched are respectively constructed into a Gaussian scale space; detecting a plurality of local extreme points in the Gaussian scale space; selecting a plurality of key points from the plurality of local extreme points, and respectively describing 72-dimensional features of the plurality of key points; by narrowing the neighborhood of the selected feature points, the dimension of the traditional SIFT descriptor is reduced, the computational complexity is reduced, and the matching speed is improved. Then, by increasing the cosine distance between the feature point vectors, the similarity between the two feature vectors to be matched can be calculated better and more effectively, and the matching precision can be improved. Finally, by means of mean-perceptual hashing operation to eliminate some mismatched point pairs, the invalid data in the sample data set is reduced, and the iterative search for the optimal parameter matrix in the matching point data set is more efficient.

Description

technical field [0001] The invention relates to the technical field of image detection, in particular to an image registration method and device based on an improved SIFT and hash algorithm. Background technique [0002] Image registration is the process of matching and superimposing two or more images acquired at different times, different sensors (imaging devices) or under different conditions, and is widely used in the fields of computer vision and image processing. Image registration mainly includes three parts: (1) feature extraction (2) feature matching (3) parameter estimation. The SIFT algorithm proposed by David Lowe has been widely used in feature extraction and matching because it remains unchanged to rotation, scaling, and brightness changes, and maintains a certain degree of stability against viewing angle changes, affine transformations, and noise. The algorithm finds feature points at different spatial scales, extracts its position, scale, and rotation invari...

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Application Information

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IPC IPC(8): G06T7/33
CPCG06T7/33
Inventor 程德强白春梦于文洁庄焕东查伟王鹏李化玉张国鹏
Owner CHINA UNIV OF MINING & TECH
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