Improved scale invariant feature transform (SIFT) image feature matching algorithm

An image feature and matching algorithm technology, applied in the field of image processing, can solve the problems such as reducing the matching speed and matching accuracy, the subsequent matching calculation amount is large, and the real-time performance cannot be satisfied, so as to achieve the effect of improving the execution efficiency and improving the execution efficiency.

Inactive Publication Date: 2013-06-05
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

[0005] Aiming at the above-mentioned prior art, the technical problem to be solved by the present invention is: there is redundant information in the 128-dimensional feature descriptor in the existi

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  • Improved scale invariant feature transform (SIFT) image feature matching algorithm
  • Improved scale invariant feature transform (SIFT) image feature matching algorithm
  • Improved scale invariant feature transform (SIFT) image feature matching algorithm

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[0042] like image 3 As shown, the two matrices represent the feature point sets of the two images to be compared. After the first match, the s solid line arrow points to the point on the second feature set that matches the first feature set. In turn, use the above algorithm to enter the second match, and find the features that have been matched in the second feature set Points correspond to matching points in the first feature set. If the result of the secondary matching is shown by the short dashed arrow, the pair of feature points is a pair of matching feature points; if the secondary matching points to other points in the first feature set, and the result is shown by the long dashed arrow, it means that the pair of feature points Dotted pairs are mismatched pairs. Through secondary matching, the accuracy of image recognition is improved.

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Abstract

The invention discloses an improved scale invariant feature transform (SIFT) image feature matching algorithm. The algorithm comprises: step one, scale space extreme points are detected; step two, feature descriptor is generated; and step three, a K-d tree balanced binary tree is built, a nearest neighborhood feature point on the K-b tree is searched by BBF, a matched feature dot pair is judged by Euclidean distance, and secondary matching is conducted after Euclidean distance matching. According to the improved SIFT image feature matching algorithm, the descriptor of 128 dimensions is reduced to 48 dimensions, execution efficiency of the algorithm is improved by two-thirds and reaches the speed of speeded-up robust features (SURF) feature description subalgorithm based on integral, and the defects that the algorithm is not suitable for the gray level and the changing circumstances of the point view of the images are overcome.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an improved SIFT image feature matching algorithm. Background technique [0002] Image matching can be divided into two categories: grayscale-based matching and feature-based matching. There are many reports on the method based on feature matching. The process of feature-based matching is: first extract the features in each image; secondly match each feature, and establish a mapping transformation between each image based on the above matched features; finally obtain the matched image. Feature contours, regions, points, and edges are image features that are often used. The selection of features has a great relationship with the content of the image. Generally speaking, the extraction of feature points is relatively easy. The feature points extracted by methods such as Hessian-Laplace, Harris-Laplace, SUSAN, DOG, Harris, etc. in related literatures have been proved to b...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 陈文宇赵艳丽屈鸿欧睿杰符明晟袁野朱建
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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