Document Type

Conference Paper

Publication Date


Publication Source

Proceedings of SPIE 9477, Optical Pattern Recognition XXVI


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.

Inclusive pages

94770C-1 to 94770C-12



Document Version

Published Version


This document is provided for download in compliance with the publisher's policy on self-archiving. Permission documentation is on file.



Society of Photo-optical Instrumentation Engineers

Place of Publication

Baltimore, MD