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Sift algorithm feature key point match based on mixing behavior ant colony algorithm

An ant colony algorithm and key point technology, applied in calculation, calculation model, image data processing, etc., can solve the problems of inability to realize large image real-time matching and slow matching speed, and achieve obvious real-time speed advantages, fast matching speed, good robustness

Inactive Publication Date: 2012-08-01
JIANGNAN UNIV
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AI Technical Summary

Problems solved by technology

However, in the traditional SIFT algorithm, when the feature points with feature descriptors are found, all points are searched in the form of traversal, which makes the matching speed slow and cannot realize real-time matching of large images.

Method used

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  • Sift algorithm feature key point match based on mixing behavior ant colony algorithm
  • Sift algorithm feature key point match based on mixing behavior ant colony algorithm
  • Sift algorithm feature key point match based on mixing behavior ant colony algorithm

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Embodiment Construction

[0024] The present invention will further introduce the embodiments of the present invention in conjunction with the accompanying drawings.

[0025] 1. Image preprocessing, pre-filtering the image to be matched to eliminate noise. If the image quality is good, you can choose not to do it. Use the median filter to preprocess the image. The median filter is a nonlinear filter that not only eliminates noise but also maintains details. A 3×3 filter window is used. In order to reduce the amount of calculation, each time the median is calculated, only the leftmost side is considered. For the pixels of , the rightmost pixel is added, and the rest of the pixels remain unchanged.

[0026] 2. Implementation steps of sift algorithm:

[0027] (1) Use different scales of Gaussian difference kernels to convolve with the original image to generate a Gaussian difference scale-space (DOG scale-space). The Gaussian difference function formula is: D(x, y, σ) = (G(x, y, kσ )-G(x, y, σ))*I(x, y...

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Abstract

Sift algorithm feature key point match based on a mixing behavior ant colony algorithm is a searching mechanism based on the mixing behavior ant colony algorithm. In a sift algorithm, after SIFT feature vectors of two images are generated, a certain key point of an image I is taken, two closest key points in an image II are found out in a traversing manner, in the two key points, and if division of a closer distance and a closest distance is smaller than a certain threshold value, the two points are judged to be a pair of matching points. If images are large, a common traversing method is slow in speed, and instantaneity is poor. Traversing operation by the aid of the mixing behavior ant colony algorithm is raised, an urban distance of feature vectors of the key points is used as similarity judgment of the key points in the two images, computing complexity is reduced, and accuracy and computing speed of key point match are greatly improved.

Description

technical field [0001] The invention relates to the fields of image matching, image splicing, digital panorama reconstruction and roaming, and the like. Disclosed is the feature key point matching method of the SIFT algorithm based on the mixed behavioral ant colony algorithm, specifically solving the feature key point matching method between two images in the SIFT algorithm (scale-invariant feature transformation algorithm), and improving the matching speed , can reach the sub-pixel level. Background technique [0002] In the 3D reconstruction of ancient cultural relics, or the establishment of large-scale game scenes, and the deployment of military defense strategies, it is hoped that through some photos or pictures, the program can automatically find the same scene in the two images, and establish the relationship between them. Correspondence, extract effective 3D information, and realize 3D restoration. The Sift algorithm is an algorithm for extracting local features, ...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T5/00G06N3/00
Inventor 陈丽芳刘渊谢振平黄秋儒刘一鸣鲁建飞杨海峰丁学东
Owner JIANGNAN UNIV