Region-of-interest-based algorithm for automatic target detection in infrared images

Shlomo Greenberg; Stanley R. Rotman; Hugo Guterman; Suzanna Zilberman; Alon Gens

doi:10.1117/1.1951547

1 July 2005 Region-of-interest-based algorithm for automatic target detection in infrared images

Shlomo Greenberg, Stanley R. Rotman, Hugo Guterman, Suzanna Zilberman, Alon Gens

Author Affiliations +

Optical Engineering, Vol. 44, Issue 7, 077002 (July 2005). https://doi.org/10.1117/1.1951547

Abstract

We present a region-of-interest-based segmentation (ROI-S) algorithm and apply it for automatic target detection. The proposed algorithm requires no templates or a priori knowledge of the targets. An automatic ROI extraction approach based on localized texture and statistical features is used to locate targets in an IR scene without any prior knowledge of their type, exact size, and orientation. Two locally adaptive histogram-based segmentation techniques are applied to extract the target signature. The Bayes decision rule is applied for a bimodal histogram while entropic correlation is used for all other cases. Geometric and statistical features are automatically extracted for each suspected ROI. We suggest a unique variance-based metric for discriminating targets from clutter and for evaluating the probability of correct detection. The proposed system is successfully tested on several hundred single-frame IR images that contain multiple examples of military vehicles, with various sizes and brightness levels and in various background scenes and orientations. A high probability of correct detection (greater than 90%) with a low false alarm rate is achieved.

©(2005) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Shlomo Greenberg, Stanley R. Rotman, Hugo Guterman, Suzanna Zilberman, and Alon Gens "Region-of-interest-based algorithm for automatic target detection in infrared images," Optical Engineering 44(7), 077002 (1 July 2005). https://doi.org/10.1117/1.1951547

Published: 1 July 2005

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