Robust image matching based on rotation and scale invariant shape context

2012 ◽  
Author(s):  
Jianfang Dou ◽  
Jianxun Li
Keyword(s):  
Image Matching ◽  
Shape Context ◽  
Scale Invariant ◽  
Robust Image ◽  
Invariant Shape

Progressive Large Scale-Invariant Image Matching in Scale Space

2017 ◽  
Author(s):  
Lei Zhou ◽  
Siyu Zhu ◽  
Tianwei Shen ◽  
Jinglu Wang ◽  
Tian Fang ◽  
...  
Keyword(s):  
Image Matching ◽  
Large Scale ◽  
Scale Space ◽  
Scale Invariant

scholarly journals PC_FFIW A Robust Image Matching Algorithm

2012 ◽  
Vol 49 (21) ◽  
pp. 20-24
Author(s):  
Behloul Ali ◽  
Aksa Abla
Keyword(s):  
Image Matching ◽  
Matching Algorithm ◽  
Robust Image

Robust image matching based on SIFT and delaunay triangulation

2012 ◽  
Vol 10 (s1) ◽  
pp. S11001-311005 ◽  
Author(s):  
Jianfang Dou Jianfang Dou ◽  
Jianxun Li Jianxun Li
Keyword(s):  
Delaunay Triangulation ◽  
Image Matching ◽  
Robust Image

Herbal Plant Classification Using Rotational and Scale Invariant Shape Based Features

2018 ◽  
Vol 16 (8) ◽  
pp. 624-631
Author(s):  
Bhupendra Fataniya ◽  
Tanish Zaveri ◽  
Sanjeev Acharya
Keyword(s):  
Scale Invariant ◽  
Herbal Plant ◽  
Invariant Shape

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.

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