Image Matching Research Based on Improved SIFT Algorithm

2019 ◽  
Author(s):  
Sidong Zhang ◽  
Xingguang Li ◽  
Dali Yin
Keyword(s):  
Image Matching ◽  
Sift Algorithm

Improved SIFT Algorithm Based on Mixed Programming

2014 ◽  
Vol 926-930 ◽  
pp. 3058-3062
Author(s):  
Dong Guang Zuo ◽  
Tao Wen ◽  
Zhong Ke Li ◽  
Zhan Liang Li
Keyword(s):  
Real Time ◽  
Image Matching ◽  
Sift Algorithm ◽  
Mixed Programming ◽  
Matching Procedure ◽  
Real Time Applications

In order to improve the generality and real-time of image matching procedure, Visual Studio 2010 and MATLAB R2009a have been used as the platform to research mixed programming and improved SIFT algorithm. In this method, the advantages of C # and Matlab have been combined to reduce the difficulty of programming and to improve programming efficiency. The results show that, improved SIFT algorithm can greatly improve real-time of matching program while guaranteeing good matching rate, its suitable in real-time applications.

scholarly journals Robust Image Matching Algorithm Using SIFT on Multiple Layered Strategies

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Yong Chen ◽  
Lei Shang ◽  
Eric Hu
Keyword(s):  
Image Matching ◽  
Geometric Feature ◽  
Data Sets ◽  
Feature Points ◽  
Matching Method ◽  
Scale Invariant ◽  
Data Set ◽  
Sift Algorithm ◽  
Delta Rule ◽  
Scale Invariant Feature

As for the unsatisfactory accuracy caused by SIFT (scale-invariant feature transform) in complicated image matching, a novel matching method on multiple layered strategies is proposed in this paper. Firstly, the coarse data sets are filtered by Euclidean distance. Next, geometric feature consistency constraint is adopted to refine the corresponding feature points, discarding the points with uncoordinated slope values. Thirdly, scale and orientation clustering constraint method is proposed to precisely choose the matching points. The scale and orientation differences are employed as the elements ofk-means clustering in the method. Thus, two sets of feature points and the refined data set are obtained. Finally, 3 * delta rule of the refined data set is used to search all the remaining points. Our multiple layered strategies make full use of feature constraint rules to improve the matching accuracy of SIFT algorithm. The proposed matching method is compared to the traditional SIFT descriptor in various tests. The experimental results show that the proposed method outperforms the traditional SIFT algorithm with respect to correction ratio and repeatability.

scholarly journals DEVELOPMENT OF NEUROMORPHIC SIFT OPERATOR WITH APPLICATION TO HIGH SPEED IMAGE MATCHING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 681-688
Author(s):  
M. Shankayi ◽  
M. Saadatseresht ◽  
M. A. V. Bitetto
Keyword(s):  
Power Consumption ◽  
Image Matching ◽  
High Speed ◽  
Feature Detection ◽  
Image Sequence ◽  
Sift Algorithm ◽  
General Design ◽  
Matching Process ◽  
Mapping Process ◽  
Speed Accuracy

There was always a speed/accuracy challenge in photogrammetric mapping process, including feature detection and matching. Most of the researches have improved algorithm's speed with simplifications or software modifications which increase the accuracy of the image matching process. This research tries to improve speed without enhancing the accuracy of the same algorithm using Neuromorphic techniques. In this research we have developed a general design of a Neuromorphic ASIC to handle algorithms such as SIFT. We also have investigated neural assignment in each step of the SIFT algorithm. With a rough estimation based on delay of the used elements including MAC and comparator, we have estimated the resulting chip's performance for 3 scenarios, Full HD movie (Videogrammetry), 24 MP (UAV photogrammetry), and 88 MP image sequence. Our estimations led to approximate 3000 fps for Full HD movie, 250 fps for 24 MP image sequence and 68 fps for 88MP Ultracam image sequence which can be a huge improvement for current photogrammetric processing systems. We also estimated the power consumption of less than10 watts which is not comparable to current workflows.

An Object Tracking Method Based on the Coordinate-Locating of SIFT Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 2178-2184
Author(s):  
Shang Bo Zhou ◽  
Kai Kang
Keyword(s):  
Image Matching ◽  
Detection Method ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Gaussian Pyramid ◽  
Number Of Layers ◽  
Invariant Feature ◽  
Comparison Time ◽  
Scale Invariant Feature ◽  
Matching Field

The SIFT (scale invariant feature transform) algorithm has been successfully used in the image matching field. In this paper, a simplified SIFT algorithm is designed. The number of layers in the Gaussian pyramid is reduced. When it is comparing the keypoints, it uses an outspreading method. The new method can reduce the comparison time and matching time. Although the new algorithm (C-SIFT algorithm) has less matching accuracy than the SIFT algorithm, it adopts a distortion detection method to abandon the wrong matching. Then it uses the coordinate displacement to determine the tracking position. Experimental results show that C-SIFT algorithm can perform steadily and timely.

Implementation of Image Matching Algorithm Based on SIFT Features

2014 ◽  
Vol 602-605 ◽  
pp. 3181-3184 ◽  
Author(s):  
Mu Yi Yin ◽  
Fei Guan ◽  
Peng Ding ◽  
Zhong Feng Liu
Keyword(s):  
Image Matching ◽  
Implementation Process ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Source Codes ◽  
Invariant Feature ◽  
Viewpoint Change ◽  
Sift Features ◽  
Scale Invariant Feature

With the aim to solve the implement problem in scale invariant feature transform (SIFT) algorithm, the theory and the implementation process was analyzed in detail. The characteristics of the SIFT method were analyzed by theory, combined with the explanation of the Rob Hess SIFT source codes. The effect of the SIFT method was validated by matching two different real images. The matching result shows that the features extracted by SIFT method have excellent adaptive and accurate characteristics to image scale, viewpoint change, which are useful for the fields of image recognition, image reconstruction, etc.

Research on Matching Pair Purification Methods of Image Based on SIFT Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1851-1854 ◽  
Author(s):  
Qiang Li ◽  
Qi Yuan Sun
Keyword(s):  
Hough Transform ◽  
Image Matching ◽  
Feature Matching ◽  
Robot Vision ◽  
Equal Division ◽  
Matching Accuracy ◽  
Vision Navigation ◽  
Sift Algorithm ◽  
Purification Methods ◽  
Matching Pair

In view of the SIFT algorithm in image matching will produce a lot of mismatches, the paper has applied a method which is based on Hough Transform will remove the SIFT matching error effectively. Firstly, to use the SIFT algorithm finish the image matching roughly. And then, using the Hough Transform to form the equal division hough units. And according to the matching parameter to distribute all the match into the hough units. The match in the units which has least matching-pair will be deleted. Experimental results show that the method can effectively improve the matching accuracy of feature matching and it lays a foundation for the following robot vision navigation.

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