Performance enhancement of various real-time image processing techniques via speculative execution

1996 ◽  
Author(s):  
Mohamed F. Younis ◽  
Purnendu Sinha ◽  
Thomas J. Marlowe ◽  
Alexander D. Stoyenko
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Performance Enhancement ◽  
Speculative Execution ◽  
Image Processing Techniques ◽  
Real Time Image Processing ◽  
Real Time Image ◽  
Processing Techniques ◽  
Time Image

scholarly journals Real-time Fuzzy Image Processing Method for Visual feedback of a Follower Robot

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Visual Feedback ◽  
Fuzzy Controller ◽  
Feedback System ◽  
Image Processing Algorithm ◽  
Real Time Image Processing ◽  
Real Time Image ◽  
Processing Techniques ◽  
Time Image

In this paper, an adaptive visual feedback system and controller has been designed and implemented in real-time to control the movements of a line follower robot to be smoother and faster. The robot consists of a couple of motorized wheels, the real-time controller and a CMOS camera as the only sensor for detecting line and feedback. The measurement based on real-time image processing and motor drive feedback used in this robot makes it robust to the obstacles and surface disturbances that may deviate robot. The image processing algorithm is adaptive to the line’s color and width too. Image processing techniques have been implemented in real-time to detect the line in the image frame and extract the necessary information (like line’s edge, coordinates and angle). A NI myRIO module is used as a stand-alone hardware unit and RT (Real-Time) target for implementation of controllers and image processing in LabVIEW environment. Both results of real-time and non-real-time implementation of controllers have been compared. To show the performance of real-time image processing in the control of this robot, three types of controllers (i.e. P, PI and Fuzzy controllers) have been implemented for line following tests and the results have been compared. At the end, it was found that the fuzzy controller controls the robot movements smoother, faster, with less errors and quicker response time compare to the other controllers

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