Image Matching Optimization Based on Taguchi Method and Adaptive Spatial Clustering with SIFT Features

2017 ◽  
Vol 31 (11) ◽  
pp. 1755014 ◽  
Author(s):  
Yuan Xu ◽  
Hehui Lu ◽  
Defu Zhou ◽  
Jiongbin Zheng ◽  
Jianguo Zhang
Keyword(s):  
Taguchi Method ◽  
Image Matching ◽  
Spatial Clustering ◽  
Signal To Noise Ratio ◽  
Matching Accuracy ◽  
Scale Invariant ◽  
Effect Factor ◽  
Optimum Parameters ◽  
Scale Invariant Feature ◽  
Selection Of

A novel image matching algorithm based on both Taguchi method and spatial clustering is proposed to optimize the Scale Invariant Feature Transform (SIFT) matching results. To improve the matching accuracy, adaptive spatial clustering is used. What is more, in order to get the fitting parameters to balance matching accuracy and quantity, Taguchi method is adopted to optimize the key parameter combination including the ratio threshold of Euclidean distance and the constrain parameters in the process of adaptive spatial clustering. Moreover, signal-to-noise ratio (SNR) results are analyzed by variance to get the effect factor which is taken as the basis for the selection of optimized parameters. The optimum parameters combination is obtained eventually. The final experimental results show that the matching quality based on SIFT feature are improved significantly.

Statistical Analysis of Experimental Parameters in Continuous Indentation Tests Using Taguchi Method

2003 ◽  
Vol 125 (4) ◽  
pp. 406-411 ◽  
Author(s):  
Eun-chae Jeon ◽  
Joo-Seung Park ◽  
Dongil Kwon
Keyword(s):  
Taguchi Method ◽  
Tensile Properties ◽  
Indentation Depth ◽  
Signal To Noise Ratio ◽  
Indentation Test ◽  
Indentation Load ◽  
Optimum Parameters ◽  
Experimental Parameters ◽  
Indentation Tests ◽  
Continuous Indentation

The continuous indentation test, which applies an indentation load to a material and records the indentation depth, yields indentation tensile properties whose accuracy can vary depending on such experimental parameters as number of unloadings, unloading ratio, maximum depth ratio and indenter radius. The Taguchi method was used to quantify their effects and to determine their optimum values. Using signal-to-noise ratio calculated from the error in the indentation tensile properties, the criterions and the optimum values for the experimental parameters were presented. The indentation tensile properties evaluated with the optimum parameters were in better agreement with the tensile properties.

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.

PERSPECTIVE-INVARIANT IMAGE MATCHING FRAMEWORK WITH BINARY FEATURE DESCRIPTOR AND APSO

2014 ◽  
Vol 28 (08) ◽  
pp. 1455011
Author(s):  
LICHUAN GENG ◽  
SONGZHI SU ◽  
DONGLIN CAO ◽  
SHAOZI LI
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Feature Points ◽  
Feature Location ◽  
Scale Invariant ◽  
Binary Descriptors ◽  
Binary Feature ◽  
Image Patches ◽  
Sample Points ◽  
Scale Invariant Feature

A novel perspective invariant image matching framework is proposed in this paper, noted as Perspective-Invariant Binary Robust Independent Elementary Features (PBRIEF). First, we use the homographic transformation to simulate the distortion between two corresponding patches around the feature points. Then, binary descriptors are constructed by comparing the intensity of sample points surrounding the feature location. We transform the location of the sample points with simulated homographic matrices. This operation is to ensure that the intensities which we compared are the realistic corresponding pixels between two image patches. Since the exact perspective transform matrix is unknown, an Adaptive Particle Swarm Optimization (APSO) algorithm-based iterative procedure is proposed to estimate the real transformation angles. Experimental results obtained on five different datasets show that PBRIEF outperforms significantly the existing methods on images with large viewpoint difference. Moreover, the efficiency of our framework is also improved comparing with Affine-Scale Invariant Feature Transform (ASIFT).

scholarly journals Portrayal Matching Algorithm By using Sift

2020 ◽  
Vol 9 (1) ◽  
pp. 2711-2713
Keyword(s):  
Image Matching ◽  
True Positive ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Image Identification ◽  
Invariant Feature ◽  
Feature Transform ◽  
Image Pairs ◽  
Matching Criteria ◽  
Scale Invariant Feature

Image identification and matching is one of the very difficult assignment in different areas of mainframe vie w. Scale-Invariant Feature Transform is an algorithm to perceive and represent specific features in portryals to further use them as an image matching criteria. In this paper, the extracted SIFT matching features are against various image distortions such as rotation, scaling, fisheye and motion distortion are evaluated and false and true positive rates for a large number of image pairs are calculated and presented.

Image Matching Algorithm for Fast Scale-Invariant Feature Transformation Based on Mask Search

2021 ◽  
Vol 58 (4) ◽  
pp. 0410010
Author(s):  
王昱皓 Wang Yuhao ◽  
唐泽恬 Tang Zetian ◽  
钟岷哲 Zhong Minzhe ◽  
王阳 Wang Yang ◽  
赵广文 Zhao Guangwen ◽  
...  
Keyword(s):  
Image Matching ◽  
Feature Transformation ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Scale Invariant Feature
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