Abstract: Methods are provided for identifying and quantifying information loss in a system due to uncertainty and analyzing the impact on the reliability of system performance. Models and methods join Fano's equality with the Data Processing Inequality in a Markovian channel construct in order to characterize information flow within a multi-component nonlinear system and allow the determination of risk and characterization of system performance upper bounds based on the information loss attributed to each component. The present disclosure additionally includes methods for estimating the sampling requirements and for relating sampling uncertainty to sensing uncertainty. The present disclosure further includes methods for determining the optimal design of components of a nonlinear system in order to minimize information loss, while maximizing information flow and mutual information.

Abstract: The present disclosure includes theoretical models and methods for identifying and quantifying information loss in a system due to uncertainty and analyzing the impact on the reliability of system performance. These models and methods join Fano's equality with the Data Processing Inequality in a Markovian channel construct in order to characterize information flow within a multi-component nonlinear system and allow the determination of risk and characterization of system performance upper bounds based on the information loss attributed to each component. The present disclosure additionally includes methods for estimating the sampling requirements and for relating sampling uncertainty to sensing uncertainty. The present disclosure further includes methods for determining the optimal design of components of a nonlinear system in order to minimize information loss, while maximizing information flow and mutual information.

Abstract: A method of quantifying computer vision algorithm performance. The method includes receiving a first image and a second image from an imaging system. Each of the first and second images is characterized by an image intensity value. Iteratively, an evaluation value of a noise profile is applies to the first and second images to form respective first and second composite images, and algorithm performance using the first and second composite images is measured. The measured performances are compared and an operational range of the algorithm determined. The noise profile includes at least one source of noise inherent to the imaging system.

Abstract: A method for registration of partially-overlapped images, comprises (a) performing noise reduction and feature extraction in a reference image and an unregistered image; (b) determining a template size using a phase transition methodology for a sufficiently-sampled finite data set; (c) identifying a template region in the reference image; (d) performing a wide angle estimation of the reference image and the unregistered image; (e) performing orientation and translation of the reference image and the unregistered image; (f) performing a search space reduction of the reference image and the unregistered image; (g) performing a coarse angle estimation of the reference image and the unregistered image; (h) performing orientation of the reference image and the unregistered image of the coarse angle estimation; and (i) performing a fine angle estimation and registration of the reference image and the unregistered image.

Abstract: A method of quantifying computer vision algorithm performance. The method includes receiving a first image and a second image from an imaging system. Each of the first and second images is characterized by an image intensity value. Iteratively, an evaluation value of a noise profile is applies to the first and second images to form respective first and second composite images, and algorithm performance using the first and second composite images is measured. The measured performances are compared and an operational range of the algorithm determined. The noise profile includes at least one source of noise inherent to the imaging system.

Abstract: An information theoretic method for testing and/or validating the suitability of a multi-radar signature database to be used on radar systems having automatic target recognition. The database may include measured data and/or modeled synthetic data. The technique allows measured data to be compared to the synthetic data using modal mutual information. The present invention further includes an information theoretic method for real time calculation of automatic target recognition using modal mutual information calculation.

Abstract: A method for testing and/or validating the suitability of a multi-radar signature database to be used on radar systems having automatic target recognition. The database may include measured field data and/or modeled synthetic data. The technique allows field data to be compared to the synthetic data using modal mutual information.