Sparsity - Inspired Recognition of Targets in Infrared

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. (a) Papers published in peer-reviewed journals (N/A for none) List of papers submitted or published that acknowledge ARO support during this reporting period. List the papers, including journal references, in the following categories: (b) Papers published in non-peer-reviewed journals or in conference proceedings (N/A for none) V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Sparsity inspired automatic target recognition,” Proceedings of SPIE 7696, 76960Q (2010). 0.00 Number of Papers published in peer-reviewed journals: Number of Papers published in non peer-reviewed journals: (c) Presentations 1.00 Number of Presentations: 0.00 Non Peer-Reviewed Conference Proceeding publications (other than abstracts): Number of Non Peer-Reviewed Conference Proceeding publications (other than abstracts): 0 Peer-Reviewed Conference Proceeding publications (other than abstracts): Vishal M Patel, Nasser M. Nasrabadi and Rama Chellappa, "Object Classification based on Simultaneous Sparse Representation", Intl. Conf. on Image Processing, Hong Kong, Sept. 2010. (d) Manuscripts Number of Peer-Reviewed Conference Proceeding publications (other than abstracts): 1 Number of Manuscripts: 0.00