Improving resolution in supervised patch-based target detection

Ron Amit, Gal Mishne, Ronen Talmon

Abstract: Recently, a supervised graph-based target detection method was proposed based on a new affinity measure between a set of target training patches and a test image. In this paper, we propose a new high-resolution detection score, which enhances the performance of the previous method by utilizing the known locations of the targets in the training images. We show that our new score is more reliable and spatially accurate, not only improving the detection resolution of true targets, but also reducing the number of false alarms. The method is successfully tested on side-scan sonar images of sea-mines, demonstrating an improved true detection rate. Our approach is general and can improve the detection resolution of the target in other patch-based detection algorithms for various signals and applications.