Probabilistic classification of elemental abundance distributions in Nakhla and Apollo 17 lunar dust samples

2006 ◽  
Author(s):  
Michael C. Storrie-Lombardi ◽  
Richard B. Hoover ◽  
Mian Abbas ◽  
G. Jerman ◽  
J. Coston ◽  
...  
Keyword(s):  
Elemental Abundance ◽  
Lunar Dust ◽  
Probabilistic Classification

Probabilistic Classification of Age by Third Molar Development: The Use of Soft Evidence

2012 ◽  
Vol 58 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Fabio Corradi ◽  
Vilma Pinchi ◽  
Iljà Barsanti ◽  
Stefano Garatti
Keyword(s):  
Third Molar ◽  
Probabilistic Classification

scholarly journals Probabilistic Classification of Tree and Shrub Vegetation on Phytogeographic System

2015 ◽  
Vol 4 (6) ◽  
Author(s):  
Thomaz Correa e Castro da Costa ◽  
Andreia Fonseca Silva ◽  
Luciana Mara Temponi de Oliveira ◽  
Joao Herbert Moreira Viana
Keyword(s):  
Probabilistic Classification

Probabilistic classification of tumour habitats in soft tissue sarcoma

2018 ◽  
Vol 31 (11) ◽  
pp. e4000 ◽  
Author(s):  
Shu Xing ◽  
Carolyn R. Freeman ◽  
Sungmi Jung ◽  
Robert Turcotte ◽  
Ives R. Levesque
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Probabilistic Classification

Real-Time Classification of Water Spray and Leaks for Robotic Firefighting

2015 ◽  
Vol 5 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Joshua G. McNeil ◽  
Brian Y. Lattimer
Keyword(s):  
Vision System ◽  
Training Dataset ◽  
Water Spray ◽  
Bayes Classifier ◽  
Test Dataset ◽  
Hazardous Environments ◽  
Water Classification ◽  
Probabilistic Classification ◽  
Suppression System

Robotic firefighting is an area of increased focus as a way of limiting the exposure of firefighters to hazardous environments. A suppression system must incorporate multiple functionalities to allow for closed-loop firefighting control. One area of development is classifying water spray as a way of correcting errors between suppressant placement and fire location. An IR vision system is presented which is capable of identifying water. Image segmentation is performed, followed by a process that classifies regions of interest as water or non-water objects. A probabilistic classification method, using Naïve Bayes classifier, was applied on a varied dataset of differing water temperatures and sprays. Objects were segmented using frame differencing with image intensity and difference thresholds. Segments were manually labeled to create a training dataset. Precision, recall, F-measure, and G-measure results of the classifier on a separate test dataset ranged from 86.1-97.4% for classifying water objects using the test dataset with water classification alone having 94.2-97.4% accuracy.

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