Shape Matching using Skeleton Context for Automated Bow Echo Detection
Mohammad Mahdi Kamani (1), Farshid Farhat (1),
Stephen Wistar (2),
James Z. Wang (1)
(1) The Pennsylvania State University, USA
(2) Accuweather Inc., USA
Abstract:
Severe weather conditions cause enormous amount of damages around the
globe. Bow echo patterns in radar images are associated with a number
of these destructive conditions such as damaging winds, hail,
thunderstorms, and tornadoes. They are detected manually by
meteorologists. In this paper, we propose an automatic framework to
detect these patterns with high accuracy by introducing novel
skeletonization and shape matching approaches. In this framework,
first we extract regions with high probability of occurring bow echo
from radar images, and apply our skeletonization method to extract the
skeleton of those regions. Next, we prune these skeletons using our
innovative pruning scheme with fuzzy logic. Then, using our proposed
shape descriptor, Skeleton Context, we can extract bow echo features
from these skeletons in order to use them in shape matching algorithm
and classification step. The output of classification indicates
whether these regions are bow echo with over 97% accuracy.
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Citation:
Mohammad Mahdi Kamani, Farshid Farhat, Stephen Wistar and James
Z. Wang, ``Shape Matching using Skeleton Context for Automated Bow
Echo Detection,'' Proceedings of the IEEE Big Data Conference,
pp. 901-908, Washington, D.C., December 2016.
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Last Modified:
December 22, 2016
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