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Blob Extraction Algorithm in Detection of Convective Cells for Data Fusion

Szuster Piotr

Journal of Telecommunications and Information Technology

2019

nr 4

65--73

Earth’s atmosphere is monitored by a multitude of sensors. It is the troposphere that is of crucial importance for human activity, as it is there that the weather phenomena take place. Weather observations are performed by surface sensors monitoring, inter alia, humidity, temperature and winds. In order to observe the developments taking place in the atmosphere, especially in the clouds, weather radars are commonly used. They monitor severe weather that is associated with storm clouds, cumulonimbuses, which create precipitation visible on radar screens. Therefore, radar images can be utilized to track storm clouds in a data fusion system. In this paper an algorithm is developed for the extraction of blobs (interesting areas in radar imagery) used within data fusion systems to track storm cells. The algorithm has been tested with the use of real data sourced from a weather radar network. 100% of convection cells were detected, with 90% of them being actual thunderstorms.

New image descriptor from edge detector and blob extractor

Grycuk R., Scherer R., Gabryel M.

Journal of Applied Mathematics and Computational Mechanics

2015

Vol. 14, nr 4

31--39

In this paper we present a novel approach for image description. The method is based on two well-known algorithms: edge detection and blob extraction. In the edge detection step we use the Canny detector. Our method provides a mathematical description of each object in the input image. On the output of the presented algorithm we obtain a histogram, which can be used in various fields of computer vision. In this paper we applied it in the content-based image retrieval system. The simulations proved the effectiveness of our method.