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Proceedings Paper

Efficient thermal image segmentation through integration of nonlinear enhancement with unsupervised active contour model
Author(s): Fatema A. Albalooshi; Evan Krieger; Paheding Sidike; Vijayan K. Asari
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Paper Abstract

Thermal images are exploited in many areas of pattern recognition applications. Infrared thermal image segmentation can be used for object detection by extracting regions of abnormal temperatures. However, the lack of texture and color information, low signal-to-noise ratio, and blurring effect of thermal images make segmenting infrared heat patterns a challenging task. Furthermore, many segmentation methods that are used in visible imagery may not be suitable for segmenting thermal imagery mainly due to their dissimilar intensity distributions. Thus, a new method is proposed to improve the performance of image segmentation in thermal imagery. The proposed scheme efficiently utilizes nonlinear intensity enhancement technique and Unsupervised Active Contour Models (UACM). The nonlinear intensity enhancement improves visual quality by combining dynamic range compression and contrast enhancement, while the UACM incorporates active contour evolutional function and neural networks. The algorithm is tested on segmenting different objects in thermal images and it is observed that the nonlinear enhancement has significantly improved the segmentation performance.

Paper Details

Date Published: 20 April 2015
PDF: 12 pages
Proc. SPIE 9477, Optical Pattern Recognition XXVI, 94770C (20 April 2015); doi: 10.1117/12.2179199
Show Author Affiliations
Fatema A. Albalooshi, Univ. of Dayton (United States)
Evan Krieger, Univ. of Dayton (United States)
Paheding Sidike, Univ. of Dayton (United States)
Vijayan K. Asari, Univ. of Dayton (United States)

Published in SPIE Proceedings Vol. 9477:
Optical Pattern Recognition XXVI
David Casasent; Mohammad S. Alam, Editor(s)

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