Sparsity Motivated Automated Target Recognition

Sparsity-based methods have recently been suggested for tasks such as face and iris recognition. In this project, we evaluated the effectiveness of such methods for automatic target recognition in infrared images. We show how sparsity can be helpful for efficient utilization of data for target recognition. We evaluated the effectiveness of the proposed algorithm in terms of recognition rate and confusion matrices on the well known Comanche forward-looking infrared (FLIR) data set consisting of ten different military targets at different orientations. This work was done in collaboration with Dr. Nasser Nasrabadi, Chief Scientist, SEDD, Army research laboratory. This work will be presented at the International Conference on Image Processing being held in Hong Kong in September 2010. A journal paper reporting our work is under preparation.