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Proceedings Paper

Detection of reflecting surfaces by a statistical model
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Paper Abstract

Remote sensing is widely used assess the destruction from natural disasters and to plan relief and recovery operations. How to automatically extract useful features and segment interesting objects from digital images, including remote sensing imagery, becomes a critical task for image understanding. Unfortunately, current research on automated feature extraction is ignorant of contextual information. As a result, the fidelity of populating attributes corresponding to interesting features and objects cannot be satisfied. In this paper, we present an exploration on meaningful object extraction integrating reflecting surfaces. Detection of specular reflecting surfaces can be useful in target identification and then can be applied to environmental monitoring, disaster prediction and analysis, military, and counter-terrorism. Our method is based on a statistical model to capture the statistical properties of specular reflecting surfaces. And then the reflecting surfaces are detected through cluster analysis.

Paper Details

Date Published: 4 February 2009
PDF: 8 pages
Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510O (4 February 2009); doi: 10.1117/12.805761
Show Author Affiliations
Qiang He, Mississippi Valley State Univ. (United States)
Chee-Hung Henry Chu, Univ. of Louisiana at Lafayette (United States)

Published in SPIE Proceedings Vol. 7251:
Image Processing: Machine Vision Applications II
Kurt S. Niel; David Fofi, Editor(s)

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