Bobby D Hunt

Address:

455 Alpine Ave, Ventura, CA 93004

Phone:

(805)6471950

VIN:

3TMLU4EN6CM093282

Make:

TOYOTA

Model:

TACOMA

Year:

2012

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:

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.