Share Email Print

Proceedings Paper

Adaptive fusion of infrared and visible images in dynamic scene
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Multiple modalities sensor fusion has been widely employed in various surveillance and military applications. A variety of image fusion techniques including PCA, wavelet, curvelet and HSV has been proposed in recent years to improve human visual perception for object detection. One of the main challenges for visible and infrared image fusion is to automatically determine an optimal fusion strategy for different input scenes along with an acceptable computational cost. This paper, we propose a fast and adaptive feature selection based image fusion method to obtain high a contrast image from visible and infrared sensors for targets detection. At first, fuzzy c-means clustering is applied on the infrared image to highlight possible hotspot regions, which will be considered as potential targets' locations. After that, the region surrounding the target area is segmented as the background regions. Then image fusion is locally applied on the selected target and background regions by computing different linear combination of color components from registered visible and infrared images. After obtaining different fused images, histogram distributions are computed on these local fusion images as the fusion feature set. The variance ratio which is based on Linear Discriminative Analysis (LDA) measure is employed to sort the feature set and the most discriminative one is selected for the whole image fusion. As the feature selection is performed over time, the process will dynamically determine the most suitable feature for the image fusion in different scenes. Experiment is conducted on the OSU Color-Thermal database, and TNO Human Factor dataset. The fusion results indicate that our proposed method achieved a competitive performance compared with other fusion algorithms at a relatively low computational cost.

Paper Details

Date Published: 29 September 2011
PDF: 8 pages
Proc. SPIE 8184, Unmanned/Unattended Sensors and Sensor Networks VIII, 818409 (29 September 2011); doi: 10.1117/12.902603
Show Author Affiliations
Guang Yang, Stevens Institute of Technology (United States)
Yafeng Yin, Stevens Institute of Technology (United States)
Hong Man, Stevens Institute of Technology (United States)
Sachi Desai, U.S. Army RDECOM (United States)

Published in SPIE Proceedings Vol. 8184:
Unmanned/Unattended Sensors and Sensor Networks VIII
Edward M. Carapezza, Editor(s)

© SPIE. Terms of Use
Back to Top