Automatic Image Processing of Locator Sensors. Main Problems of Processing Radar, Sonar and Optoelectronic Images

2021 ◽  
Author(s):  
A. Y. Zilinberg ◽  
Y. A. Korneev
Keyword(s):  
Image Processing ◽  
Automatic Image Processing

EMCAT: An automatic image-processing system for electron-microscopic tomography

1994 ◽  
Vol 52 ◽  
pp. 418-419
Author(s):  
Weiping Liu ◽  
John W. Sedat ◽  
David A. Agard
Keyword(s):  
Image Processing ◽  
Structural Information ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Processing System ◽  
Electron Microscopic ◽  
Data Set ◽  
Ordered Structures ◽  
Automatic Image Processing ◽  
Em Tomography

Any real world object is three-dimensional. The principle of tomography, which reconstructs the 3-D structure of an object from its 2-D projections of different view angles has found application in many disciplines. Electron Microscopic (EM) tomography on non-ordered structures (e.g., subcellular structures in biology and non-crystalline structures in material science) has been exercised sporadically in the last twenty years or so. As vital as is the 3-D structural information and with no existing alternative 3-D imaging technique to compete in its high resolution range, the technique to date remains the kingdom of a brave few. Its tedious tasks have been preventing it from being a routine tool. One keyword in promoting its popularity is automation: The data collection has been automated in our lab, which can routinely yield a data set of over 100 projections in the matter of a few hours. Now the image processing part is also automated. Such automations finish the job easier, faster and better.

scholarly journals A preclinical ultrasound method for the assessment of vascular disease progression in murine models

Ultrasound ◽  
2018 ◽  
Vol 27 (2) ◽  
pp. 85-93
Author(s):  
Justyna Janus ◽  
Baris Kanber ◽  
Wadhah Mahbuba ◽  
Charlotte Beynon ◽  
Kumar V Ramnarine ◽  
...  
Keyword(s):  
Image Processing ◽  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Vascular Disease ◽  
Mouse Models ◽  
Murine Models ◽  
Analysis Method ◽  
Manual Analysis ◽  
Abdominal Aortic ◽  
Automatic Image Processing

Introduction The efficacy of preclinical ultrasound at providing a quantitative assessment of mouse models of vascular disease is relatively unknown. In this study, preclinical ultrasound was used in combination with a semi-automatic image processing method to track arterial distension alterations in mouse models of abdominal aortic aneurysm and atherosclerosis. Methods Longitudinal B-mode ultrasound images of the abdominal aorta were acquired using a preclinical ultrasound scanner. Arterial distension was assessed using a semi-automatic image processing algorithm to track vessel wall motion over the cardiac cycle. A standard, manual analysis method was applied for comparison. Results Mean arterial distension was significantly lower in abdominal aortic aneurysm mice between day 0 and day 7 post-onset of disease (p < 0.01) and between day 0 and day 14 (p < 0.001), while no difference was observed in sham control mice. Manual analysis detected a significant decrease (p < 0.05) between day 0 and day 14 only. Atherosclerotic mice showed alterations in arterial distension relating to genetic modification and diet. Arterial distension was significantly lower (p < 0.05) in Ldlr−/− (++/−−) mice fed high-fat western diet when compared with both wild type (++/++) mice and Ldlr−/− (++/−−) mice fed chow diet. The manual method did not detect a significant difference between these groups. Conclusions Arterial distension can be used as an early marker for the detection of arterial disease in murine models. The semi-automatic analysis method provided increased sensitivity to differences between experimental groups when compared to the manual analysis method.

Determination of the fracture toughness by automatic image processing

1996 ◽  
Vol 78 (1) ◽  
pp. 35-44 ◽  
Author(s):  
J. Stampfl ◽  
S. Scherer ◽  
M. Berchthaler ◽  
M. Gruber ◽  
O. Kolednik
Keyword(s):  
Image Processing ◽  
Fracture Toughness ◽  
Automatic Image Processing

scholarly journals An automatic image processing algorithm for initiating and terminating intracoronary 
OFDI pullback

2010 ◽  
Vol 1 (2) ◽  
pp. 566 ◽  
Author(s):  
Lida P. Hariri ◽  
Brett E. Bouma ◽  
Sergio Waxman ◽  
Milen Shishkov ◽  
Benjamin J. Vakoc ◽  
...  
Keyword(s):  
Image Processing ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Automatic Image Processing

A New Security and Safety Solution for Public Guided Transport

2009 ◽  
Author(s):  
Jo¨rg Schu¨tte ◽  
Sven Scholz
Keyword(s):  
Image Processing ◽  
Public Transport ◽  
Processing System ◽  
Incident Management ◽  
Robust Solution ◽  
Critical Areas ◽  
Safety Research ◽  
Automatic Image Processing ◽  
Development Plans ◽  
Management Capabilities

Railway and Public Guided Transit Properties often employ large numbers of video cameras to supervise critical areas and facilitate incident management. Capabilities of Central Control Staff is, however, limited to check the increasing number of CCTV images and so far automated image processing solutions had been insufficiently reliable. TelSys GmbH (a railway telematics company in Dresden, Germany) had therefore developed over the last seven years together with the University of Technology in Dresden and some public transport providers (subway of Berlin, subway of Prague) a robust solution to supervise automatically critical areas like tunnel entrances, station tracks or station platform edges. Also qualifications with German Railways and in Finland had been performed. The automatic image processing software reliably differentiates between trains (“permitted” objects) and objects that move from the platform into the tracks or move too close to otherwise prohibited areas. Object sizes, alarm times, reliability and safety requirements had been taken from the VDV 399 standard of the German Public Transport Operators Association. After years of reliability and safety research and demonstration the system is now in regular operation (stopping automatically incoming driverless trains if an object is detected in the track) and can be considered as the first safe video image processing system according to railway standards. Experiences, system architecture and principles as well as further development plans and planned demonstration installation in North America are discussed.

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