Automatic target recognition from surveillance images using phase mutual information

In this paper we introduce and test a new similarity measure for use in a template matching process for target detection and recognition. The measure has recently been developed for multi-modal registration of medical images and is known as phase mutual information (PMI). The key advantage of PMI is that it is invariant to lighting conditions, the ratio between foreground and background intensity and the level of background clutter, which is critical for target detection and recognition from the surveillance images acquired from various sensors. Several experiments were conducted using real and synthetic datasets to evaluate the performance of PMI when compared with a number of commonly used similarity measures including mean squared difference, gradient error and intensity mutual information. Our results show that PMI consistently provided the most accurate detection and recognition performance.