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A Fast Image SIFT Feature Matching Method Based on GPU and Cascade Hash

A feature matching and image technology, applied in the field of computer vision, can solve problems such as a large amount of computing time, and achieve the effect of shortening matching time and powerful parallel computing capabilities

Active Publication Date: 2020-01-21
HUAZHONG UNIV OF SCI & TECH
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Problems solved by technology

However, in the case of a large number of images to be matched, such as 3D reconstruction of large scenes, some scenes require pairwise matching between images, then there are order of magnitude of matching pairs to process, where N p is the number of images, that is, in the case of massive image matching tasks, the cascade hash matching method still requires a lot of computing time

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  • A Fast Image SIFT Feature Matching Method Based on GPU and Cascade Hash
  • A Fast Image SIFT Feature Matching Method Based on GPU and Cascade Hash
  • A Fast Image SIFT Feature Matching Method Based on GPU and Cascade Hash

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specific Embodiment approach

[0029] Such as figure 1 Shown is the overall flow diagram of the method of the embodiment of the present invention. From figure 1 It can be seen that this method includes the establishment of a hard disk, memory, and GPU three-level exchange mechanism, the upper triangle block reading method, a rough hash screening module, and a fine hash screening and output module. Its specific implementation is as follows:

[0030] (1) According to the GPU global video memory size limit and memory size limit, all image feature point data are divided into blocks and grouped, and a three-level exchange mechanism of GPU, memory, and hard disk is established at the computer level;

[0031] (2) Propose and use an improved GPU parallel protocol method, make full use of the three-level cache mechanism of global video memory, shared memory, and registers at the internal level of the GPU, and perform two hash maps with different encoding lengths on all SIFT feature points of the image;

[0032] (...

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Abstract

The invention discloses a fast image SIFT feature matching method based on GPU and cascaded hash, which belongs to the field of computer vision. According to the limitation of GPU global video memory size and memory size, the present invention establishes a three-level exchange mechanism of GPU, memory, and hard disk; at the same time, an improved GPU parallel protocol method is adopted to perform two hash maps with different encoding lengths on all SIFT feature points of the image ; proposed and used the upper triangular matrix block reading method; rough screening by local sensitive hash algorithm; fine screening of candidate points by calculating Hamming distance; finally, by calculating the Euclidean distance between the screening point and the point to be matched to find the most Appropriate matching point; using the asynchronous parallel method of CPU and GPU, the matching result data of the image pair is copied from the GPU memory to the memory and saved to the disk while the GPU is operating. The present invention greatly shortens the matching time of SIFT feature points, and can complete feature matching in a short time even in the case of massive data.

Description

technical field [0001] The invention belongs to the field of computer vision, and more specifically relates to a fast image SIFT feature matching method based on GPU and cascaded hashing. Background technique [0002] Image feature matching is the corresponding relationship of feature points between images, which has a wide range of applications in image recognition, image registration and 3D reconstruction. The SIFT feature has scale invariance and rotation invariance, so it has strong anti-interference ability and high matching accuracy, so it is a commonly used feature for image feature matching. However, each SIFT feature point is represented by a 128-dimensional vector. The similarity measure of the SIFT feature point is the Euclidean distance. In the case of a high-dimensional vector, the Euclidean distance calculation time is longer, and the linear search method matches between two images. The computational complexity is O(N 2 ), where N is the average number of fea...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/46G06T1/20
CPCG06T1/20G06V10/462G06F18/2148G06F18/24147
Inventor 陶文兵徐涛孙琨
Owner HUAZHONG UNIV OF SCI & TECH
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