Improved target detector for FLIR imagery
Algorithms are considered for searching wide area forward-looking infrared imagery for military vehicles. Wide area search has typically been handled by using a simple detection algorithm with low computational cost to search the entire image or set of images, followed by a clutter rejection algorithm that analyzes only those portions of the image that are marked by the detection algorithm. We start with a feature based detector and a eigen-neural based clutter rejecter, and examine a number of architectures for combining these modules to maximize joint performance. The architectures considered include a clutter rejection threshold method and a nonlinear learning-based combination. The performance of the architectures are compared using a set of several thousand real images.