@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 {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}
}
@article {12681,
	title = {Dimensionality Estimation in Hyperspectral Imagery Using Minimum Description Length},
	year = {2004},
	month = {2004/12//},
	institution = {MARYLAND UNIV COLLEGE PARK CENTER FOR AUTOMATION RESEARCH},
	abstract = {Numerous algorithms have been developed for hyperspectral automatic target recognition (ATR) applications. Many of these algorithms require estimation of a background subspace. The estimation of the background subspace has been addressed using multiple methods. but most of these methods assume a-priori knowledge of the background dimensionality. In order to automate the estimation of the background subspace. we present an algorithm based on minimum description length (MDL) that can identify the background dimension. Results show that the MDL criterion estimates the proper dimension of the background for ATR applications.},
	keywords = {*ALGORITHMS, *HYPERSPECTRAL IMAGERY, *TARGET DETECTION, *TARGET RECOGNITION, AUTOMATIC, BURIED OBJECTS, COMPONENT REPORTS, MINE DETECTION, NUMERICAL MATHEMATICS, SURFACE TARGETS., SYMPOSIA, TARGET DIRECTION, RANGE AND POSITION FINDING},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA431643},
	author = {Broadwater,Joshua B and Meth,Reuven and Chellapa, Rama}
}
@article {15004,
	title = {Optimal Systolic Designs for the Computation of the Discrete Hartley and the Discrete Cosine Transforms},
	year = {1988},
	month = {1988///},
	institution = {Institute for Systems Research, University of Maryland, College Park},
	abstract = {In this paper, we propose new algorithms for computing the Discrete Hartley and the Discrete Cosine Transform. The algorithms are based on iterative applications of the modified small n algorithms of DFT. The one dimensional transforms are mapped into two dimensions first and then implemented on two dimensional systolic arrays. Pipelined bit serial architectures operating on left to right LSB to MSB binary arithmetic is the basis of the hardware design. Different hardware schemes for implementing these transforms are studied. We show that our schemes achieve a substantial speed-up over existing schemes.},
	keywords = {*ALGORITHMS, *BINARY ARITHMETIC, Computer architecture, DCT(DISCRETE COSINE TRANSFORM), DISCRETE FOURIER TRANSFORMS, ITERATIONS, NUMERICAL MATHEMATICS, TWO DIMENSIONAL},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA452389},
	author = {Chakrabarti,Chaitali and JaJa, Joseph F.}
}
@article {15014,
	title = {Parallel Algorithms for Wiring Module Pins to Frame Pads},
	year = {1988},
	month = {1988///},
	institution = {Institute for Systems Research, University of Maryland, College Park},
	abstract = {We present fast and efficient parallel algorithms for several problems related to wiring a set of pins on a module to a set of pads lying on the houndary of a chip. The one-layer model is used to perform the wiring. Our basic model of parallel processing is the CREW-PRAM model which is characterized by the presence of an unlimited number processors sharing the main memory. Concurrent reads are allowed while concurrent writes are not. All our algorithms use 0(n) processors, where n is the input length. Our algorithms have fast implementations on other parallel models such as the mesh or the hypercube.},
	keywords = {*ALGORITHMS, *PARALLEL PROCESSING, COMPUTER PROGRAMMING AND SOFTWARE, efficiency, INPUT, LAYERS, length, MEMORY DEVICES, MODELS, MODULAR CONSTRUCTION, NUMERICAL MATHEMATICS, PARALLEL ORIENTATION, PINS, WIRE},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA452383},
	author = {Chang,Shing-Chong and JaJa, Joseph F.}
}
@article {15671,
	title = {On Finding Shortest Paths on Convex Polyhedra.},
	year = {1985},
	month = {1985/05//},
	institution = {CENTER FOR AUTOMATION RESEARCH, UNIVERSITY OF MARYLAND COLLEGE PARK},
	abstract = {Applications in robotics and autonomous navigation have motivated the study of motion planning and obstacle avoidance algorithms. The special case considered here is that of moving a point (the object) along the surface of a convex polyhedron (the obstacle) with n vertices. Sharir and Schorr have developed an algorithm that, given a source point on the surface of a convex polyhedron, determines the shortest path from the source to any point on the polyhedron in linear time after O(n cubed log n) preprocessing time. The preprocessed output requires O(n squared) space. By using known algorithms for fast planar point location, the shortest path query time for Sharir and Schorr{\textquoteright}s algorithm is shown to be O(k + log n) where k is the number faces traversed by the path. We give an improved preprocessing algorithm that runs in O(n squared log n) time requiring the same query time and space. We also show how to store the output of the preprocessing algorithm in O(n log n) space while maintaining the same query time. (Author)},
	keywords = {*ALGORITHMS, *POLYGONS, *Polyhedrons, *PREPROCESSING, AUTONOMOUS NAVIGATION, AVOIDANCE., BARRIERS, INTERROGATION, MOTION, output, PATHS, PE61102F, planning, POSITION(LOCATION), ROBOTICS, THEORETICAL MATHEMATICS, TIME},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA166246},
	author = {Mount, Dave}
}
