Neuristics, Inc.

Hughes Electronics Corporation

Datron/transoc, Inc.

Bobby R Hunt

Venkatesh Narayanan

Subramaniam Mohan

Timothy L Hutcheson

Ramanujam Srinivasan

Peter G Wohlmut

Wilson Or

Thomas W Ryan

Gerard L Rafanelli

Susan B Mount

Stephen K Johnson

James F Montgomery

Philip J Sementilli

Albert M Bisbee

A spinning strip aperture imaging radiometer sensor system and data processing method for synthesizing a super-resolved scene estimate (super-resolved scene) from a plurality of image frames acquired by the strip aperture imaging sensor system. One embodiment of the imaging system comprises a rotating strip aperture wide field of view telescope, a two dimensional detector array for detecting images in the focal plane of the telescope, rotation compensation apparatus for preventing rotational smear during the integration time of the detectors, a signal processor for recording a plurality of image frames of a scene that is imaged by the telescope as it rotates around its optical axis, and an estimation processor employing the present method for synthesizing the super-resolved scene estimate from the recorded images. The super-resolved image synthesis method uses a plurality of rotating strip aperture measurements within the strip aperture passband and within the passband of an equivalent bandlimited synthesized full circular aperture to estimate the spatial frequency information outside the total measurement passband, and/or outside the passband of the equivalent bandlimited synthesized full circular aperture, as well as within the equivalent bandlimited passband. Knowledge of the spatial response function of the strip aperture, the spatial response function of the detector array, noise statistics, and the temporal registrations of each of the recorded strip aperture images permits synthesis of the super-resolved full aperture image by the sensor system and image synthesis method. The super-resolved image synthesis method may be employed in the spatial domain, the spatial frequency domain, or both.

Super-resolved full aperture scene synthesis using rotating strip

A method and apparatus under software control for pattern recognition utilizes a neural network implementation to recognize two dimensional input images which are sufficiently similar to a database of previously stored two dimensional images. Images are first image processed and subjected to a Fourier transform which yields a power spectrum. An in-class to out-of-class study is performed on a typical collection of images in order to determine the most discriminatory regions of the Fourier transform. A feature vector consisting of the highest order (most discriminatory) magnitude information from the power spectrum of the Fourier transform of the image is formed. Feature vectors are input to a neural network having preferably two hidden layers, input dimensionality of the number of elements in the feature vector and output dimensionality of the number of data elements stored in the database. Unique identifier numbers are preferably stored along with the feature vector. Application of a query feature vector to the neural network will result in an output vector. The output vector is subjected to statistical analysis to determine if a sufficiently high confidence level exists to indicate that a successful identification has been made. Where a successful identification has occurred, the unique identifier number may be displayed.

Pattern recognition system

A pattern recognition method and apparatus utilizes a neural network to recognize input images which are sufficiently similar to a database of previously stored images. Images are first processed and subjected to a Fourier transform which yields a power spectrum. An in-class to out-of-class study is performed on a typical collection of images in order to determine the most discriminatory regions of the Fourier transform. A feature vector consisting of the (most discriminatory) information from the power spectrum of the Fourier transform of the image is formed. Feature vectors are input to a neural network having preferably two hidden layers, input dimensionality of the number of elements in the feature vector and output dimensionality of the number of data elements stored in the database. Unique identifier numbers are preferably stored along with the feature vector. Application of a query feature vector to the neural network results in an output vector. The output vector is subjected to statistical analysis to determine if a sufficiently high confidence level exists to indicate a successful identification whereupon a unique identifier number may be displayed.

Method and apparatus for determining and organizing feature vectors for

Apparatus for generating a feature matrix based on normalized out-class

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Tucson, AZ, United States of America

Bobby R Hunt