A Data-Fusion Scheme for Quantitative Image Analysis by Using Locally Weighted Regression and Dempster–Shafer Theory

2008 ◽  
Vol 57 (11) ◽  
pp. 2554-2560 ◽  
Author(s):  
Zheng Liu ◽  
D.S. Forsyth ◽  
M.-S. Safizadeh ◽  
A. Fahr
Keyword(s):  
Image Analysis ◽  
Data Fusion ◽  
Quantitative Image Analysis ◽  
Weighted Regression ◽  
Locally Weighted Regression ◽  
Dempster Shafer Theory ◽  
Quantitative Image ◽  
Shafer Theory ◽  
Fusion Scheme

Digital image acquisition, processing, and analysis in a polymer microscopy laboratory

1994 ◽  
Vol 52 ◽  
pp. 454-455
Author(s):  
Vinod K. Berry ◽  
Xiao Zhang
Keyword(s):  
United States ◽  
Image Analysis ◽  
Digital Image ◽  
Image Acquisition ◽  
Image Transmission ◽  
The United States ◽  
Quantitative Image Analysis ◽  
Quantitative Image

In recent years it became apparent that we needed to improve productivity and efficiency in the Microscopy Laboratories in GE Plastics. It was realized that digital image acquisition, archiving, processing, analysis, and transmission over a network would be the best way to achieve this goal. Also, the capabilities of quantitative image analysis, image transmission etc. available with this approach would help us to increase our efficiency. Although the advantages of digital image acquisition, processing, archiving, etc. have been described and are being practiced in many SEM, laboratories, they have not been generally applied in microscopy laboratories (TEM, Optical, SEM and others) and impact on increased productivity has not been yet exploited as well.In order to attain our objective we have acquired a SEMICAPS imaging workstation for each of the GE Plastic sites in the United States. We have integrated the workstation with the microscopes and their peripherals as shown in Figure 1.

scholarly journals Quantitative image analysis of microbial communities with BiofilmQ

2021 ◽  
Author(s):  
Raimo Hartmann ◽  
Hannah Jeckel ◽  
Eric Jelli ◽  
Praveen K. Singh ◽  
Sanika Vaidya ◽  
...  
Keyword(s):  
Image Analysis ◽  
Microbial Communities ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Software Tool ◽  
Image Cytometry ◽  
Quantitative Image Analysis ◽  
Space And Time ◽  
Quantitative Image ◽  
Three Dimensional Space

AbstractBiofilms are microbial communities that represent a highly abundant form of microbial life on Earth. Inside biofilms, phenotypic and genotypic variations occur in three-dimensional space and time; microscopy and quantitative image analysis are therefore crucial for elucidating their functions. Here, we present BiofilmQ—a comprehensive image cytometry software tool for the automated and high-throughput quantification, analysis and visualization of numerous biofilm-internal and whole-biofilm properties in three-dimensional space and time.

Quantitative Image Analysis of Nuclear Chromatin Distribution for Cytological Diagnosis

2011 ◽  
Vol 55 (5) ◽  
pp. 455-459 ◽  
Author(s):  
Ryotaro Jingu ◽  
Masafumi Ohki ◽  
Sumiko Watanabe ◽  
Sadafumi Tamiya ◽  
Setsuo Sugishima ◽  
...  
Keyword(s):  
Image Analysis ◽  
Quantitative Image Analysis ◽  
Cytological Diagnosis ◽  
Nuclear Chromatin ◽  
Quantitative Image

Decision Support System for Advanced Traffic Management Through Data Fusion

2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng
Keyword(s):  
Data Fusion ◽  
Traffic Management ◽  
Information Sources ◽  
Partial Information ◽  
Weather Conditions ◽  
Field Testing ◽  
Traffic Operations ◽  
Dempster Shafer Theory ◽  
Classification Technique ◽  
Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

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