@article {12469,
	title = {Sparsity Motivated Automated Target Recognition},
	year = {2010},
	month = {2010/09/29/},
	abstract = {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.},
	keywords = {*ALGORITHMS, *INFRARED IMAGES, *TARGET RECOGNITION, ARMY RESEARCH, efficiency, FORWARD LOOKING INFRARED SYSTEMS, IMAGE PROCESSING, NUMERICAL MATHEMATICS, OPTICS, PE611102, TARGET DETECTION, TARGET DIRECTION, RANGE AND POSITION FINDING},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA535014},
	author = {Chellapa, Rama}
}
@article {12470,
	title = {Sparsity-Inspired Recognition of Targets in Infrared Images},
	year = {2010},
	month = {2010/09/29/},
	abstract = {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.},
	keywords = {*FORWARD LOOKING INFRARED SYSTEMS, *IMAGE PROCESSING, *TARGET RECOGNITION, algorithms, ARMY RESEARCH, ATR(AUTOMATIC TARGET RECOGNITION), AUTOMATIC, CNN(CONVENTIONAL NEURAL NETWORKS), CS(COMPRESSIVE SENSING), DISCRIMINANT ANALYSIS, FLIR(FORWARD LOOKING INFRARED), INFRARED DETECTION AND DETECTORS, INFRARED IMAGES, LDA(LINEAR DISCRIMINANT ANALYSIS), MILITARY OPERATIONS, MNN(MODULAR NEURAL NETWORKS), neural nets, PCA(PRINCIPAL COMPONENT ANALYSIS), PE611102, SPARSITY, TARGET DIRECTION, RANGE AND POSITION FINDING},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA535424},
	author = {Chellapa, Rama}
}
@article {14506,
	title = {Statistical Relational Learning as an Enabling Technology for Data Acquisition and Data Fusion in Heterogeneous Sensor Networks},
	year = {2008},
	month = {2008/06/29/},
	institution = {OFFICE OF RESEARCH ADMINISTRATION AND ADVANCEMENT, UNIVERSITY OF MARYLAND COLLEGE PARK},
	abstract = {Our work has focused on developing new cost sensitive feature acquisition and classification algorithms, mapping these algorithms onto camera networks, and creating a test bed of video data and implemented vision algorithms that we can use to implement these. First, we will describe a new algorithm that we have developed for feature acquisition in Hidden Markov Models (HMMs). This is particularly useful for inference tasks involving video from a single camera, in which the relationship between frames of video can be modeled as a Markov chain. We describe this algorithm in the context of using background subtraction results to identify portions of video that contain a moving object. Next, we will describe new algorithms that apply to general graphical models. These can be tested using existing test sets that are drawn from a range of domains in addition to sensor networks.},
	keywords = {*ALGORITHMS, *CLASSIFICATION, data acquisition, DATA FUSION, Detectors, Feature extraction, HMM(HIDDEN MARKOV MODELS), NETWORKS, NUMERICAL MATHEMATICS, PE611102, RANDOM FIELDS, STATISTICS AND PROBABILITY, TEST SETS, VIDEO SIGNALS},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA500520},
	author = {Jacobs, David W. and Getoor, Lise}
}
