@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 {14914,
	title = {Uncertainty Propagation in Model-Based Recognition.},
	year = {1994},
	month = {1994/12//},
	institution = {MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LAB},
	abstract = {Building robust recognition systems requires a careful understanding of the effects of error in sensed features. Error in these image features results in a region of uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty region when model poses are based on matching three image and model points, for both Gaussian and bounded error in the detection of image points, and for both scaled-orthographic and perspective projection models. This result applies to objects that are fully three-dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute the uncertainty region when poses are based on any number of initial matches. Finally, we use these results to extend, from two-dimensional to three-dimensional objects, robust implementations of alignment interpretation-tree search, and transformation clustering. (AN)},
	keywords = {*IMAGE PROCESSING, *PATTERN RECOGNITION, algorithms, APPROXIMATION(MATHEMATICS), CYBERNETICS, ERROR CORRECTION CODES, image registration, Linear programming, MATCHING, MATHEMATICAL MODELS, PIXELS, PROJECTIVE TECHNIQUES., regions, THREE DIMENSIONAL, TWO DIMENSIONAL, Uncertainty},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA295642},
	author = {Jacobs, David W. and Alter,T. D.}
}
@article {12051,
	title = {Computing Intrinsic Images.},
	year = {1986},
	month = {1986/08//},
	abstract = {Low level modern computer vision is not domain dependent, but concentrates on problems that correspond to identifiable modules in the human visual system. Several theories have been proposed in the literature for the computation of shape from shading, shape from texture, retinal motion from spatiotemporal derivatives of the image intensity function and the like. The problems with the existing approach are basically the following: (1) The employed assumptions are very strong and so most of the algorithms fail when applied to real images. (2) Usually the constraints from the geometry and the physics of the problem are not enough to guarantee uniqueness of the computed parameters. (3) In most cases the resulting algorithms are not robust, in the sense that if there is a slight error in the input this results in a catastrophic error in the output. In this thesis the problem of machine vision is explored from its basics. A low level mathematical theory is presented for the unique robust computation of intrinsic parameters. The computational aspect of the theory envisages a cooperative highly parallel implementation, bringing in information from five different sources (shading, texture, motion, contour and stereo), to resolve ambiguities and ensure uniqueness and stability of the intrinsic parameters. The problems of shape from texture, shape from shading and motion, visual motion analysis and shape and motion from contour are analyzed in detail.},
	keywords = {*ARTIFICIAL INTELLIGENCE, *COMPUTERS, *IMAGE PROCESSING, *VISION, algorithms, CAMERAS, CATASTROPHIC CONDITIONS, COMPUTATIONS, CYBERNETICS, ERRORS, HUMANS, IMAGES, INTENSITY, LOW LEVEL, MATHEMATICS, MOTION, RETINA, Robots, SHADOWS, SHAPE, TEXTURE, THEORY, THESES.},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA189440},
	author = {Aloimonos, J.}
}
@article {11984,
	title = {Determining the Translation of a Rigidly Moving Surface, without Correspondence,},
	year = {1986},
	month = {1986/01//},
	abstract = {A method is presented for the recovery of the three-dimensional translation of a rigidly moving textured object from its images. The novelty of the method consists of the fact that four cameras are used in order to avoid the solution of the correspondence problem. The method seems to be immune to small noise percentages and to have good behavior when the noise increases. Keywords: Computer vision.},
	keywords = {*IMAGE PROCESSING, *OPTICAL DETECTION, *Translation(Image processing), CAMERAS, Computer vision, COMPUTERS, IMMUNITY., MOTION, MOVING TARGETS, NOISE, OPTICAL DETECTION AND DETECTORS, SURFACES, THREE DIMENSIONAL, VISION},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA179409},
	author = {Aloimonos, J. and Basu, Anup}
}
