mathematical morphology
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2022 ◽  
Vol 15 (1) ◽  
pp. 23-44
Author(s):  
Santiago Velasco-Forero ◽  
R. Pagès ◽  
Jesus Angulo

Author(s):  
Alexsandra Oliveira Andrade ◽  
Flaulles Boone Bergamaschi ◽  
Roque Mendes Prado Trindade ◽  
Regivan Hugo Nunes Santiago

Author(s):  
Mazouzi Amine ◽  
Kerfa Djoudi ◽  
Ismail Rakip Karas

<span lang="EN-US">In this article, a new method of vehicles detecting and tracking is presented: A thresholding followed by a mathematical morphology treatment are used. The tracking phase uses the information about a vehicle. An original labeling is proposed in this article. It helps to reduce some artefacts that occur at the detection level. The main contribution of this article lies in the possibility of merging information of low level (detection) and high level (tracking). In other words, it is shown that many artefacts resulting from image processing (low level) can be detected, and eliminated thanks to the information contained in the labeling (high level). The proposed method has been tested on many video sequences and examples are given illustrating the merits of our approach.</span>


Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.


2021 ◽  
Author(s):  
Jun LING ◽  
Zhenying XU ◽  
Ziqian WU ◽  
Qiling LI ◽  
Mengyu TANG ◽  
...  

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