Bobby Ray Hunt

US Patent:

51612046, Nov 3, 1992

Filed:

Jun 4, 1990

Appl. No.:

7/533113

Inventors:

Timothy L. Hutcheson - Los Gatos CA
Wilson Or - Santa Clara CA
Venkatesh Narayanan - Fremont CA
Subramaniam Mohan - Sunnyvale CA
Peter G. Wohlmut - Saratoga CA
Ramanujam Srinivasan - Sunnyvale CA
Bobby R. Hunt - Tucson AZ
Thomas W. Ryan - Tucson AZ

Assignee:

Neuristics, Inc. - Milpitas CA

International Classification:

G06K 946
G06K 966
G06K 954
G06K 960

US Classification:

382 16

Abstract:

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.

US Patent:

54653088, Nov 7, 1995

Filed:

Aug 25, 1993

Appl. No.:

8/111616

Inventors:

Timothy L. Hutcheson - Los Gatos CA
Wilson Or - Santa Clara CA
Venkatesh Narayanan - Fremont CA
Subramaniam Mohan - Sunnyvale CA
Peter G. Wohlmut - Saratoga CA
Ramanujam Srinivasan - Sunnyvale CA
Bobby R. Hunt - Tucson AZ
Thomas W. Ryan - Tucson AZ

Assignee:

Datron/Transoc, Inc. - Simi Valley CA

International Classification:

G06K 962

US Classification:

382159

Abstract:

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.

US Patent:

61557048, Dec 5, 2000

Filed:

Apr 19, 1996

Appl. No.:

8/635073

Inventors:

Bobby R. Hunt - Tucson AZ
Gerard L. Rafanelli - Fountain Valley CA
Philip J. Sementilli - Tucson AZ
Susan B. Mount - Torrance CA
Albert M. Bisbee - Tucson AZ
James F. Montgomery - Hermosa Beach CA
Stephen K. Johnson - Herndon VA

Assignee:

Hughes Electronics - El Segundo CA

International Classification:

G01S 1389
G01S 1390

US Classification:

364525

Abstract:

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.

US Patent:

52747140, Dec 28, 1993

Filed:

Jul 23, 1992

Appl. No.:

7/920188

Inventors:

Timothy L. Hutcheson - Los Gatos CA
Wilson Or - Santa Clara CA
Venkatesh Narayanan - Fremont CA
Subramaniam Mohan - Sunnyvale CA
Peter G. Wohlmut - Saratoga CA
Ramanujam Srinivasan - Sunnyvale CA
Bobby R. Hunt - Tucson AZ
Thomas W. Ryan - Tucson AZ

Assignee:

Neuristics, Inc. - Milpitas CA

International Classification:

G06K 962
G06K 946
G06K 968
G06K 936

US Classification:

382 15

Abstract:

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.