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29 August 2016 Characterizing the time-frequency image by morphological pattern spectrum for evaluating electromagnetic environment complexity
Bing Li, Shuang-shuang Chen, Peng-yuan Liu, Jun Dong
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100332O (2016) https://doi.org/10.1117/12.2244319
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
Abstract
A new feature extraction scheme for evaluating the complexity of Electromagnetic Environment (EME) is presented based on the generalized S transform and morphological pattern spectrum (MPS) in this study. Firstly, the EME signals were transformed to time-frequency image (TFI) by the generalized S transform, which combines the separate strengths of the short-time Fourier transform and wavelet transforms. Secondly, the MPS, which has been widely used in image processing area, is employed to characterize the TFI of EME signals. We also investigated the influence of structure element (SE) to the MPS. Four types of SE, mean the line SE, square SE, diamond SE and circle SE, are utilized and compared for computing the MPS. EME signals with four complexity degree are simulated to evaluate the effectiveness of the presented method. Experimental results have revealed that the presented feature extraction scheme is an effective tool for discriminating the complexity of EME.
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Bing Li, Shuang-shuang Chen, Peng-yuan Liu, and Jun Dong "Characterizing the time-frequency image by morphological pattern spectrum for evaluating electromagnetic environment complexity", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100332O (29 August 2016); https://doi.org/10.1117/12.2244319
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KEYWORDS
Feature extraction
Time-frequency analysis
Diamond
Electromagnetism
Fractal analysis
Fourier transforms
Image segmentation
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