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.

The Application of SIFT Algorithm in Blind Road Environmental Image Matching

2012 ◽  
Vol 155-156 ◽  
pp. 1137-1141
Author(s):  
Shuo Shi ◽  
Ming Yu ◽  
Cui Hong Xue ◽  
Ying Zhou
Keyword(s):  
Feature Extraction ◽  
Image Matching ◽  
Feature Points ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Extraction Algorithm ◽  
Environmental Image ◽  
Scale Invariant Feature

Image Matching is a key technology in the intelligent navigation system for the blind, which is based on the computer video. The images of moving blind people, collected at real time, have variety of changes in light, rotation, scaling, etc. Against this feature, we propose a practical matching algorithm, which is based on the SIFT (Scale Invariant, Feature Transform). That is the image matching algorithm. We focus on the algorithm of the SIFT feature extraction and matching, and obtain the feature points of the image through feature extraction algorithm. We verify the effect of the algorithm by selecting practical images with rotation, scaling and different light. The result is that this method can get better matches for the blind road environmental image.

The Study of Reduce the Mismatch Based on the SIFT Feature Matching

2012 ◽  
Vol 580 ◽  
pp. 378-382
Author(s):  
Xiao Yu Liu ◽  
Yan Piao ◽  
Lei Liu
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Space Rotation ◽  
Feature Transform ◽  
The Difference ◽  
Scale Invariant Feature ◽  
Keypoints Matching

The algorithm of SIFT (scale-invariant feature transform) feature matching is an international hotspot in the areas of the keypoints matching and target recognition at the present time. The algorithm is used in the image matching widely because of the good invariance of scale, illumination and space rotation .This paper proposes a new theory to reduce the mismatch—the theory to reduce the mismatch based on the main orientation of keypoints. This theory should firstly compute the grads of the main orientation of a couple of matched keypoints in the two images and the difference between them. Because the difference of the main orientation of matched keypoints should be much larger than the couples which are matched correctly, we can distinguish and reduce the mismatch through setting the proper threshold, and it can improve the accuracy of the SIFT algorithm greatly.

Research on Image Matching Combining on Fractional Differential With Scale Invariant Feature Transform

2015 ◽  
Author(s):  
Guimei Zhang ◽  
Binbin Chen ◽  
YangQuan Chen
Keyword(s):  
Image Matching ◽  
Scale Invariant Feature Transform ◽  
Fractional Differentiation ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Self Similar ◽  
Fractional Differential ◽  
Feature Transform ◽  
Scale Invariant Feature

Image matching is one of the most important problems in computer vision. Scale Invariant Feature Transform (SIFT) algorithm has been proved to be effective for detecting features for image matching. However SIFT algorithm has limitation to extract features in textile image or self-similar construction image. Fortunately fractional differentiation has advantage to strengthen and extract textural features of digital images. Aiming at the problem, this paper proposes a new method for image matching based on fractional differentiation and SIFT. The method calculates the image pyramid combining the Riemann-Liouville (R-L) fractional differentiation and the derivative of the Gaussian function. Thus image feature has been enhanced, and more feature points can be extracted. As a result the matching accuracy is improved. Additionally, a new feature detection mask based on fractional differential is constructed. The proposed method is a significant extension of SIFT algorithm. The experiments carried out with images in database and real images indicate that the proposed method can obtain good matching results. It can be used for matching textile image or some self-similar construct image.

Medical Image Feature Matching Based on Wavelet Transform and SIFT Algorithm

2011 ◽  
Vol 65 ◽  
pp. 497-502
Author(s):  
Yan Wei Wang ◽  
Hui Li Yu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Medical Image ◽  
Feature Matching ◽  
Image Feature ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Scale Invariant Feature

A feature matching algorithm based on wavelet transform and SIFT is proposed in this paper, Firstly, Biorthogonal wavelet transforms algorithm is used for medical image to delaminating, and restoration the processed image. Then the SIFT (Scale Invariant Feature Transform) applied in this paper to abstracting key point. Experimental results show that our algorithm compares favorably in high-compressive ratio, the rapid matching speed and low storage of the image, especially for the tilt and rotation conditions.

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.

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