scholarly journals SIFT Feature Matching Algorithm with Local Shape Context

2013 ◽  
Vol 5 (20) ◽  
pp. 4810-4815
Author(s):  
Gu Lichuan ◽  
Qiao Yulong ◽  
Cao Mengru ◽  
Guo Qingyan
Keyword(s):  
Feature Matching ◽  
Shape Context ◽  
Local Shape ◽  
Matching Algorithm ◽  
Sift Feature

Reference Point-Based SIFT Feature Matching

2014 ◽  
Vol 543-547 ◽  
pp. 2670-2673
Author(s):  
Lei Cao ◽  
Di Liao ◽  
Bin Dang Xue
Keyword(s):  
Reference Point ◽  
Image Matching ◽  
Euclidean Distance ◽  
Feature Matching ◽  
Experimental Results ◽  
Feature Descriptor ◽  
Matching Algorithm ◽  
Sift Feature ◽  
Sift Features ◽  
Suitable Reference

Aiming to solve the high computational and time consuming problem in SIFT feature matching, this paper presents an improved SIFT feature matching algorithm based on reference point. The algorithm starts from selecting a suitable reference point in the feature descriptor space when SIFT features are extracted. In the feature matching stage, this paper uses the Euclidean distance between descriptor vectors of the feature point to be matched and the reference point to make a fast filtration which removes most of the features that could not be matched. For the remaining SIFT features, Best-bin-first (BBF) algrithm is utilized to obtain precise matches. Experimental results demonstrate that the proposed matching algorithm achieves good effectiveness in image matching, and takes only about 60 percent of the time that the traditional matching algorithm takes.

SIFT feature matching algorithm of multi-source remote image

2013 ◽  
Vol 21 (8) ◽  
pp. 2146-2153 ◽  
Author(s):  
刘志文 LIU Zhi-wen ◽  
刘定生 LIU Ding-sheng ◽  
刘鹏 LIU Peng
Keyword(s):  
Feature Matching ◽  
Matching Algorithm ◽  
Sift Feature

scholarly journals Fast SIFT Feature Matching Algorithm Based on Geometric Transformation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 88133-88140
Author(s):  
Yongchao Wang ◽  
Yijun Yuan ◽  
Zhao Lei
Keyword(s):  
Feature Matching ◽  
Geometric Transformation ◽  
Matching Algorithm ◽  
Sift Feature

A novel feature matching algorithm based on shape-context between optical and infrared image

2009 ◽  
Author(s):  
Zenglu Wan ◽  
Zheng Li ◽  
Jianliang Weng ◽  
Hongxia Gao
Keyword(s):  
Feature Matching ◽  
Infrared Image ◽  
Shape Context ◽  
Matching Algorithm

SIFT feature matching algorithm based on second moment matrix

2011 ◽  
Vol 31 (1) ◽  
pp. 29-32
Author(s):  
Jin-qin ZHONG ◽  
Jie-qing TAN ◽  
Ying-ying LI ◽  
Li-chuan GU
Keyword(s):  
Feature Matching ◽  
Moment Matrix ◽  
Matching Algorithm ◽  
Sift Feature ◽  
Second Moment

The Optimization of SIFT Feature Matching Algorithm on Face Recognition Based on BP Neural Network

2015 ◽  
Vol 743 ◽  
pp. 359-364 ◽  
Author(s):  
B. Liao ◽  
H.F. Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Feature Matching ◽  
Illumination Variation ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Sift Feature ◽  
Identification Rate ◽  
Candidate Image ◽  
Matching Efficiency

In the field of object recognition, the SIFT feature is known to be a very successful local invariant descriptor and has wide application in different domains. However it also has some limitations, for example, in the case of facial illumination variation or under large tilt angle, the identification rate of the SIFT algorithm drops quickly. In order to reduce the probability of mismatching pairs, and improve the matching efficiency of SIFT algorithm, this paper proposes a novel feature matching algorithm. The basic idea is taking the successful-matched SIFT feature points as the training samples to establish a space mapping model based on BP neural network. Then, with the help of this model, the estimated coordinate of the corresponding SIFT feature point in the candidate image is predicted. Finally search the possible matching points around the coordinate. The experiment results show that using the prediction model, the number of mismatching points can be reduced effectively and the number of correct matching pairs increases at the same time

A SIFT Feature Matching Algorithm Based on Semi-Variance Function

2013 ◽  
Vol 647 ◽  
pp. 896-900 ◽  
Author(s):  
Feng Tian ◽  
Yu Bo Yan
Keyword(s):  
High Dimension ◽  
Image Matching ◽  
Feature Matching ◽  
Variance Function ◽  
Feature Point ◽  
Matching Algorithm ◽  
Sift Feature ◽  
Matching Efficiency

For solving the low matching efficiency problem due to high dimension of eigenvector in SIFT, a SIFT feature matching algorithm based on semi-variance function is proposed. For each feature point in image SIFT feature point zone, m beams are generated by using the position of the feature point as center and the orientation of the feature point as start direction. The image semi-variance function value of each beam, which is treated as SIFT value of eigenvector descriptor, is used in the algorithm aiming at reducing the dimension of eigenvector and improving image matching efficiency. The experiment result shows that the matching rate of this algorithm is higher, the matching time of this algorithm is less.

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