Optimization method of SIFT characteristic matching points based on limit restraint

A feature matching and limit constraint technology, applied in the field of image matching, can solve the problems of matching process error, low matching rate, incorrect and so on

Inactive Publication Date: 2014-09-03
SUZHOU UNIV OF SCI & TECH
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Problems solved by technology

However, no matter which algorithm is used, there is a possibility of errors in the matching process. Therefore, eliminating wrong point pairs during the matching process plays an important role in improving the matching rate.
[0003] After matching the local features extracted by Scale-invariant feature transform (SIFT) in the case of camera translation, the feature points are detected in SIFT, and the feature descriptor is calculated. The Euclidean distance for the feature de

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  • Optimization method of SIFT characteristic matching points based on limit restraint

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Embodiment 1

[0022] like figure 1 As shown, the SIFT feature matching point optimization method based on limit constraints of the present invention specifically includes the following steps:

[0023] 1. Detect all SIFT feature points in each image, and extract the 128-dimensional descriptor vector of each feature point.

[0024] 2. Match the feature points in Image0 and Image1: use the Euclidean distance between vectors to find the two feature points nearest and next to each feature point in Image0 in the feature points of Image1, and calculate the nearest and next distances Dis0 and Dis1, and record Dis0 / Dis1 as ratio; at this time, each feature point in Image0 has a ratio value (ratio>0).

[0025] 3. Among all the feature points of Image0, find out all the points with ratio<0.382 and store them in the point set V1.

[0026] 4. Calculate the pixel distance between each feature point and the nearest feature point in Image1 in V1, and store the feature points with 0

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Abstract

The invention discloses an optimization method of SIFT characteristic matching points based on limit restraint, and aims at overcoming disadvantages of SIFT characteristic matching points. Obtained matching points at present are divided into a high-quality point set V11, a standard point set V12 and a to-be-measured point set V13, the standard point set V12 and to-be-measured point set V13 are updated, the high-quality point set V11, the updated standard point set V12 and the updated to-be-measured point set V13 are combined, a basic camera matrix F is calculated again in an RANSAC method, and exterior points are rejected. Thus, the matching rate is effectively improved, the computational complexity in the budgeting process is low, and computation is rapid.

Description

technical field [0001] The invention belongs to the category of image matching technology in the field of image processing and pattern recognition, and in particular relates to a method for optimizing SIFT feature matching points based on limit constraints. Background technique [0002] Image matching technology is mainly a method to find the same image target through the corresponding relationship, similarity and consistency analysis of image content, features, structure, relationship, texture and gray level. Image matching technology can be divided into: matching technology based on image grayscale, matching technology based on image features, matching technology based on template matching and matching technology based on transform domain. The basic idea of ​​grayscale matching: regard the image as a two-dimensional signal from a statistical point of view, use the statistical correlation method to find the correlation match between the signals, and use the correlation func...

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Application Information

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IPC IPC(8): G06T7/00G06K9/46
Inventor 胡伏原董治方吴宏杰
Owner SUZHOU UNIV OF SCI & TECH
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