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Optical Engineering

Classification of moving objects in atmospherically degraded video
Author(s): Eli Chen; Oren Haik; Yitzhak Yitzhaky
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Paper Abstract

Classification of moving objects in imaging through long-distance atmospheric path may be affected by distortions such as blur and spatiotemporal movements caused by air turbulence. This work aims to study and quantify the effects of these distortions on the ability to classify moving objects in atmospherically degraded video signals. For this purpose, we perform simulations and examine real long-range thermal video cases. In the simulation, we evaluate various geometrical (shape-based) object features for classification at different distortion levels. Furthermore, we examine the influence of image restoration on the classification performances in the real-degraded videos, using geometrical and textural features (combined and in separate) of the objects. Principal component analysis together with both k-nearest neighbor and support vector machines is used for the classification process. Results show how classification performances decrease as the level of blur increases, and how successful digital image restoration for real cases can significantly improve the classification performances.

Paper Details

Date Published: 20 June 2012
PDF: 15 pages
Opt. Eng. 51(10) 101710 doi: 10.1117/1.OE.51.10.101710
Published in: Optical Engineering Volume 51, Issue 10
Show Author Affiliations
Eli Chen, Ben-Gurion Univ. of the Negev (Israel)
Oren Haik, Ben-Gurion Univ. of the Negev (Israel)
Yitzhak Yitzhaky, Ben-Gurion Univ. of the Negev (Israel)

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