scholarly journals Comparison of image matching techniques

2016 ◽  
Vol 7 (3) ◽  
Keyword(s):  
Image Matching ◽  
Matching Techniques

scholarly journals In vivo kinematics of gait in posterior-stabilized and bicruciate-stabilized total knee arthroplasties using image-matching techniques

2018 ◽  
Vol 42 (11) ◽  
pp. 2573-2581 ◽  
Author(s):  
Koji Murakami ◽  
Satoshi Hamai ◽  
Ken Okazaki ◽  
Yifeng Wang ◽  
Satoru Ikebe ◽  
...  
Keyword(s):  
Image Matching ◽  
Total Knee Arthroplasties ◽  
Posterior Stabilized ◽  
In Vivo Kinematics ◽  
Total Knee ◽  
Matching Techniques

Angle-distance image matching techniques for protein structure comparison

2008 ◽  
Vol 21 (6) ◽  
pp. 442-452 ◽  
Author(s):  
Chia-Han Chu ◽  
Chuan Yi Tang ◽  
Cheng-Yin Tang ◽  
Tun-Wen Pai
Keyword(s):  
Protein Structure ◽  
Image Matching ◽  
Structure Comparison ◽  
Protein Structure Comparison ◽  
Matching Techniques

scholarly journals Multi Sensor Image Matching using Super Symmetric Affinity Tensors based HyperGraph Matching

2019 ◽  
Vol 8 (2) ◽  
pp. 6161-6166
Keyword(s):  
Image Matching ◽  
Third Order ◽  
Good Recall ◽  
Hypergraph Matching ◽  
Sensing Applications ◽  
Performance Accuracy ◽  
Matching Technique ◽  
Multiple Sensor ◽  
Remote Sensing Applications ◽  
Matching Techniques

Image Matching technique is regularly on one of the main errands in numerous Photogrammetry and Remote Sensing applications. Based on multi-discipline, the approach of multiple sensor image matching is a novel one established which has vital application in military, civil, medicinal, and certain other domains. However, image matching approach faces numerous challenges, specifically in multi-sensor images where the images are gathered from the different sensor with different intensities, scales, and moments. Thus, a novel image matching approach is introduced in this paper using affinity tensor and HyperGraph Matching (HGM) technique that attempts to overcome certain drawbacks in matching and increases performance accuracy. Hypergraph matching techniques are employed using affinity tensors and consider supersymmetric property during construction. Graphs are constructed using graph theory for both sources, and target image and matching is done using third-order tensors. The experimental outcomes displayed that the proposed technique has good recall, precision, and positive accuracy values compared to the existing two descriptors based and tensor-based matching algorithms.

Evaluation of image matching techniques for optical fiber specklegram sensor analysis

2018 ◽  
Vol 57 (33) ◽  
pp. 9845 ◽  
Author(s):  
Eric Fujiwara ◽  
Yoshi Ri ◽  
Yu Tzu Wu ◽  
Hiroshi Fujimoto ◽  
Carlos Kenichi Suzuki
Keyword(s):  
Optical Fiber ◽  
Image Matching ◽  
Sensor Analysis ◽  
Matching Techniques
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