A 10 000-Image-per-Second Parallel Algorithm for Real-Time Detection of MARFEs on JET

This paper presents a very high-speed image processing algorithm applied to multi-faceted asymmetric radiation from the edge (MARFE) detection on the Joint European Torus. The algorithm was built in serial and parallel versions and written in C/C+ using OpenCV, cvBlob, and LibSVM libraries. The code...

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Bibliographic Details
Published in:IEEE transactions on plasma science Vol. 41; no. 2; pp. 341 - 349
Main Authors: Portes de Albuquerque, Marcio, Murari, Andrea, Giovani, M., Alves, Nilton, Portes de Albuquerque, Marcelo
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
Cameras
Codes
Computer architecture
Identification
Image processing
Image processing systems
Libraries
multi-faceted asymmetric radiation from the edge detection
multi-faceted asymmetric radiation from the edge identification
Operating systems
parallel algorithm
Real-time systems
Time measurement
Videos
ISSN:0093-3813, 1939-9375
Online Access:Get full text
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Summary:This paper presents a very high-speed image processing algorithm applied to multi-faceted asymmetric radiation from the edge (MARFE) detection on the Joint European Torus. The algorithm was built in serial and parallel versions and written in C/C+ using OpenCV, cvBlob, and LibSVM libraries. The code implemented was characterized by its accuracy and run-time performance. The final result of the parallel version achieves a correct detection rate of 97.6% for MARFE identification and an image processing rate of more than 10 000 frame per second. The parallel version divides the image processing chain into two groups and seven tasks. One group is responsible for Background Image Estimation and Image Binarization modules, and the other is responsible for region Feature Extraction and Pattern Classification. At the same time and to maximize the workload distribution, the parallel code uses data parallelism and pipeline strategies for these two groups, respectively. A master thread is responsible for opening, signaling, and transferring images between both groups. The algorithm has been tested in a dedicated Intel symmetric-multiprocessing computer architecture with a Linux operating system.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0093-3813
1939-9375
DOI:10.1109/TPS.2013.2237927