A New Multiscale Line Detection Approach for Aerial Image with Complex Scene

Author(s):  
Jing Wang ◽  
Takeshi Ikenaga ◽  
Satoshi Goto ◽  
Kazuo Kunieda ◽  
Makoto Iwata ◽  
...  

Author(s):  
Thorsten Wagner ◽  
Luca Lusnig ◽  
Sabrina Pospich ◽  
Markus Stabrin ◽  
Fabian Schönfeld ◽  
...  

AbstractStructure determination of filamentous molecular complexes involves the selection of filaments from cryo-EM micrographs. The automatic selection of helical specimens is particularly difficult and thus many challenging samples with issues such as contamination or aggregation are still manually picked. Here we present two approaches for selecting filamentous complexes: one uses a trained deep neural network to identify the filaments and is integrated in SPHIRE-crYOLO, the other one, called SPHIRE-STRIPER, is based on a classical line detection approach. The advantage of the crYOLO based procedure is that it accurately performs on very challenging data sets and selects filaments with high accuracy. Although STRIPER is less precise, the user benefits from less intervention, since in contrast to crYOLO, STRIPER does not require training. We evaluate the performance of both procedures on tobacco mosaic virus and filamentous F-actin data sets to demonstrate the robustness of each method.



2020 ◽  
Vol 76 (7) ◽  
pp. 613-620
Author(s):  
Thorsten Wagner ◽  
Luca Lusnig ◽  
Sabrina Pospich ◽  
Markus Stabrin ◽  
Fabian Schönfeld ◽  
...  

Structure determination of filamentous molecular complexes involves the selection of filaments from cryo-EM micrographs. The automatic selection of helical specimens is particularly difficult, and thus many challenging samples with issues such as contamination or aggregation are still manually picked. Here, two approaches for selecting filamentous complexes are presented: one uses a trained deep neural network to identify the filaments and is integrated in SPHIRE-crYOLO, while the other, called SPHIRE-STRIPER, is based on a classical line-detection approach. The advantage of the crYOLO-based procedure is that it performs accurately on very challenging data sets and selects filaments with high accuracy. Although STRIPER is less precise, the user benefits from less intervention, since in contrast to crYOLO, STRIPER does not require training. The performance of both procedures on Tobacco mosaic virus and filamentous F-actin data sets is described to demonstrate the robustness of each method.



2018 ◽  
Vol 78 (1) ◽  
pp. 1053-1066
Author(s):  
Xiaoliang Jiang ◽  
Xiaojun Yang ◽  
Zhengen Ying ◽  
Liwen Zhang ◽  
Jie Pan ◽  
...  






Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.



2010 ◽  
Vol 130 (11) ◽  
pp. 2039-2046
Author(s):  
Munetoshi Numada ◽  
Masaru Shimizu ◽  
Takuma Funahashi ◽  
Hiroyasu Koshimizu


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