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

2006 ◽  
Author(s):  
Jing Wang ◽  
Takeshi Ikenaga ◽  
Satoshi Goto ◽  
Kazuo Kunieda ◽  
Makoto Iwata ◽  
...  
Keyword(s):  
Aerial Image ◽  
Line Detection ◽  
Complex Scene ◽  
Detection Approach

scholarly journals Two particle picking procedures for filamentous proteins: SPHIRE-crYOLO filament mode and SPHIRE-STRIPER

2020 ◽  
Author(s):  
Thorsten Wagner ◽  
Luca Lusnig ◽  
Sabrina Pospich ◽  
Markus Stabrin ◽  
Fabian Schönfeld ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Molecular Complexes ◽  
High Accuracy ◽  
The Other ◽  
Line Detection ◽  
Data Sets ◽  
Detection Approach ◽  
Selection Of

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.

scholarly journals Two particle-picking procedures for filamentous proteins: SPHIRE-crYOLO filament mode and SPHIRE-STRIPER

2020 ◽  
Vol 76 (7) ◽  
pp. 613-620
Author(s):  
Thorsten Wagner ◽  
Luca Lusnig ◽  
Sabrina Pospich ◽  
Markus Stabrin ◽  
Fabian Schönfeld ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Molecular Complexes ◽  
High Accuracy ◽  
The Other ◽  
Line Detection ◽  
Data Sets ◽  
Detection Approach ◽  
Selection Of

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.

Segmentation of shallow scratches image using an improved multi-scale line detection approach

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

New mechanisms for the less-is-more effect: A signal detection approach

2009 ◽  
Author(s):  
Konstantinos V. Katsikopoulos
Keyword(s):  
Signal Detection ◽  
Less Is More ◽  
Detection Approach

Faster line detection algorithms on enhanced mesh connected arrays

1993 ◽  
Vol 140 (2) ◽  
pp. 95 ◽  
Author(s):  
Y. Pan ◽  
H.Y.H. Chuang
Keyword(s):  
Line Detection ◽  
Detection Algorithms

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

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.

Real-Time Line Detection Method for Camera Images of the Rescue Robot

2010 ◽  
Vol 130 (11) ◽  
pp. 2039-2046
Author(s):  
Munetoshi Numada ◽  
Masaru Shimizu ◽  
Takuma Funahashi ◽  
Hiroyasu Koshimizu
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Line Detection ◽  
Time Line ◽  
Rescue Robot
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