Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28135
Title: SEMO-MAMO, a 3-phase module to compare tropical cyclone satellite images using a modified Hausdorff distance
Authors: Feng, BO
Liu, JNK
Keywords: Edge detection
Geophysics computing
Image matching
Meteorology
Remote sensing
Satellite telemetry
Issue Date: 2004
Publisher: IEEE
Source: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 26-29 August 2004, v. 6, p. 3808-3813 How to cite?
Journal: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 26-29 August 2004 
Abstract: Tropical cyclones (TC) are natural geoscientific phenomena which affect the daily lives of people around the world. Traditionally, the identification of TCs has been highly dependent on human subjective justification on vast supply of information, such as Dvorak templates. In this paper, we present an efficient three-phase prototype SEMO-MAMO. This uses a modified Hausdorff distance based on the extracted contour edges with predefined weighted points to compare the TC satellite images. A new formulation of the Hausdorff distance is designed for significance-based points matching. The experimental results have shown that the proposed prototype improves the computational speed and matching accuracy. It provides another efficient way to use the Hausdorff distance measure for tropical cyclone satellite images matching and prediction.
URI: http://hdl.handle.net/10397/28135
ISBN: 0-7803-8403-2
DOI: 10.1109/ICMLC.2004.1380495
Appears in Collections:Conference Paper

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