Image segmentation using K-means color quantization and density-based spatial clustering of applications with noise (DBSCAN) for hotspot detection in photovoltaic modules

2016 ◽  
Author(s):  
Genevieve C. Ngo ◽  
Erees Queen B. Macabebe
Keyword(s):  
Image Segmentation ◽  
Spatial Clustering ◽  
Color Quantization ◽  
Hotspot Detection ◽  
Photovoltaic Modules

Using Volunteered Geographic Information to Assess the Spatial Distribution of West Nile Virus in Detroit, Michigan

2013 ◽  
pp. 1170-1182
Author(s):  
Kevin P. McKnight ◽  
Joseph P. Messina ◽  
Ashton M. Shortridge ◽  
Meghan D. Burns ◽  
Bruce W. Pigozzi
Keyword(s):  
West Nile Virus ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
The United States ◽  
Data Availability ◽  
Limited Information ◽  
Hotspot Detection ◽  
West Nile ◽  
Dead Bird ◽  
Global And Local

West Nile Virus is a vector-borne flavivirus that affects mainly birds, horses, and humans. The disease emerged in the United States in 1999 and by 2001 had reached Michigan. In clinical human cases, the most common symptoms are fever, weakness, nausea, headache, and changes in mental state. The crow is the most common wildlife host in the life cycle of the virus. The state of Michigan, through the Michigan Department of Community Health, collected the spatial locations of over 8,000 dead birds (Corvidae), statewide, during 2002. The large number of samples made spatial and temporal hotspot detection possible. However, the volunteer reporting method produced a dataset with a direct correlation between the numbers and locations of the dead birds and human population density and accurately identifying hotspots remains a challenge. Geographic variation in dead bird intensity was modeled using both global and local spatial clustering algorithms. Statistical models identified overall spatial structure and local clustering. Identification of hotspots was confounded by limited information about the collection procedures, data availability and quality, and the limitations of each method.

A Spatial Clustering Method With Edge Weighting for Image Segmentation

2013 ◽  
Vol 10 (5) ◽  
pp. 1124-1128 ◽  
Author(s):  
Nan Li ◽  
Hong Huo ◽  
Yu-ming Zhao ◽  
Xi Chen ◽  
Tao Fang
Keyword(s):  
Image Segmentation ◽  
Spatial Clustering ◽  
Clustering Method ◽  
Edge Weighting
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