Comparison of additive image fusion vs. feature-level image fusion techniques for enhanced night driving

Edward J. Bender; Colin E. Reese; Gooitzen S. Van Der Wal

doi:10.1117/12.450867

5 February 2003 Comparison of additive image fusion vs. feature-level image fusion techniques for enhanced night driving

Edward J. Bender, Colin E. Reese, Gooitzen S. Van Der Wal

Author Affiliations +

Proceedings Volume 4796, Low-Light-Level and Real-Time Imaging Systems, Components, and Applications; (2003) https://doi.org/10.1117/12.450867
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

The Night Vision & Electronic Sensors Directorate (NVESD) has conducted a series of image fusion evaluations under the Head-Tracked Vision System (HTVS) program. The HTVS is a driving system for both wheeled and tracked military vehicles, wherein dual-waveband sensors are directed in a more natural head-slewed imaging mode. The HTVS consists of thermal and image-intensified TV sensors, a high-speed gimbal, a head-mounted display, and a head tracker. A series of NVESD field tests over the past two years has investigated the degree to which additive (A+B) image fusion of these sensors enhances overall driving performance. Additive fusion employs a single (but user adjustable) fractional weighting for all the features of each sensor's image. More recently, NVESD and Sarnoff Corporation have begun a cooperative effort to evaluate and refine Sarnoff's "feature-level" multi-resolution (pyramid) algorithms for image fusion. This approach employs digital processing techniques to select at each image point only the sensor with the strongest features, and to utilize only those features to reconstruct the fused video image. This selection process is performed simultaneously at multiple scales of the image, which are combined to form the reconstructed fused image. All image fusion techniques attempt to combine the "best of both sensors" in a single image. Typically, thermal sensors are better for detecting military threats and targets, while image-intensified sensors provide more natural scene cues and detect cultural lighting. This investigation will address the differences between additive fusion and feature-level image fusion techniques for enhancing the driver's overall situational awareness.

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Edward J. Bender, Colin E. Reese, and Gooitzen S. Van Der Wal "Comparison of additive image fusion vs. feature-level image fusion techniques for enhanced night driving", Proc. SPIE 4796, Low-Light-Level and Real-Time Imaging Systems, Components, and Applications, (5 February 2003); https://doi.org/10.1117/12.450867

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