Fast Automatic Visibility Optimization for Thermal Synthetic Aperture Visualization
This work addresses the need for efficient and reliable thermal imaging in autonomous search and rescue operations using camera drones, representing a domain-specific advancement.
The authors tackled the problem of manual parameter optimization in thermal synthetic aperture visualization by introducing the first fully automatic method, proving that target visibility is proportional to image variance, which is invariant to occlusion, and validated this approach to replace time-consuming and error-prone manual exploration.
In this article, we describe and validate the first fully automatic parameter optimization for thermal synthetic aperture visualization. It replaces previous manual exploration of the parameter space, which is time consuming and error prone. We prove that the visibility of targets in thermal integral images is proportional to the variance of the targets' image. Since this is invariant to occlusion it represents a suitable objective function for optimization. Our findings have the potential to enable fully autonomous search and recuse operations with camera drones.