Multifractal detrended fluctuation analysis parallel optimization strategy based on openMP for image processing

In the past few years, multifractal detrended fluctuation analysis (MF-DFA) method has been widely applied in the field of agricultural image processing. However, the agricultural image feature MF-DFA analyses involves a great deal of iterative processes and complex matrix operations, which require...

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Veröffentlicht in:Neural computing & applications Jg. 32; H. 10; S. 5599 - 5608
Hauptverfasser: Tang, Xiaoyong, Yang, Xiaopan, Wu, Fan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.05.2020
Springer Nature B.V
Schlagworte:
Advances in Parallel and Distributed Computing for Neural Computing
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Image processing
Image Processing and Computer Vision
Image segmentation
Iterative methods
Optimization
Performance evaluation
Probability and Statistics in Computer Science
Hurst parameter
Multifractal detrended fluctuation analysis
Parallel optimization
OpenMP
ISSN:0941-0643, 1433-3058
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Zusammenfassung:In the past few years, multifractal detrended fluctuation analysis (MF-DFA) method has been widely applied in the field of agricultural image processing. However, the agricultural image feature MF-DFA analyses involves a great deal of iterative processes and complex matrix operations, which require massive computation and processing time. In order to reduce processing time and improve analysis efficiency, we first develop a MF-DFA program that involves image preprocessing, image segmentation, local area accumulation matrix calculation, local area trend fitting, local area trend elimination, a global q th-order fluctuation function, and the Hurst index. Then, we analyze and compare MF-DFA each modules’ performance characteristics and explore its parallelism according to various segmentation scales s . Lastly, we propose a parallel optimization scheme based on OpenMP for the MF-DFA. The results of our rigorous performance evaluation clearly demonstrate that our proposed parallel optimization scheme can efficiently use multicore capability to extract rape leaf image texture characteristics.
Bibliographie:ObjectType-Article-1
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04164-2