scholarly journals Optical Flow Based Block Matching Method for Tendon Tracking in Ultrasound

2016 ◽  
Author(s):  
Bo-I Chuang ◽  
Jian-Han Hsu ◽  
Li-Chieh Kuo ◽  
Fong-Chin Su ◽  
I-Ming Jou ◽  
...  
Keyword(s):  
Optical Flow ◽  
Block Matching ◽  
Matching Method

scholarly journals Template Check and Block Matching Method for Automatic Defects Detection of the Back Light Unit

2006 ◽  
Vol 13B (4) ◽  
pp. 377-382
Author(s):  
Chang-Ho Han ◽  
Sang-Hee Cho ◽  
Choon-Suk Oh ◽  
Young-Kee Ryu
Keyword(s):  
Block Matching ◽  
Matching Method

scholarly journals A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment

2018 ◽  
Vol 14 (6) ◽  
pp. 155014771878175 ◽  
Author(s):  
Shahrukh Ashraf ◽  
Priyanka Aggarwal ◽  
Praveen Damacharla ◽  
Hong Wang ◽  
Ahmad Y Javaid ◽  
...  
Keyword(s):  
Optical Flow ◽  
Unmanned Aerial Vehicle ◽  
Global Positioning System ◽  
Motion Vector ◽  
Block Matching ◽  
Positioning System ◽  
Simulation Studies ◽  
Flow Measurements ◽  
Global Positioning ◽  
Aerial Vehicle

The ability of an autonomous unmanned aerial vehicle to navigate and fly precisely determines its utility and performance. The current navigation systems are highly dependent on the global positioning system and are prone to error because of global positioning system signal outages. However, advancements in onboard processing have enabled inertial navigation algorithms to perform well during short global positioning system outages. In this article, we propose an intelligent optical flow–based algorithm combined with Kalman filters to provide the navigation capability during global positioning system outages and global positioning system–denied environments. Traditional optical flow measurement uses block matching for motion vector calculation that makes the measurement task computationally expensive and slow. We propose the application of an artificial bee colony–based block matching technique for faster optical flow measurements. To effectively fuse optical flow data with inertial sensors output, we employ a modified form of extended Kalman filter. The modifications make the filter less noisy by utilizing the redundancy of sensors. We have achieved an accuracy of ~95% for all non-global positioning system navigation during our simulation studies. Our real-world experiments are in agreement with the simulation studies when effects of wind are taken into consideration.

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