US5465308A
Expired / Lapsed
US
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Pattern recognition system
Assignee: Datron Advanced Technologies Inc
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Filing: 1993-08-25
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Granted: 1995-11-07
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Expires: 2013-08-25
Abstract
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. 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.
Inventors
- 👤 Timothy L. Hutcheson
- 👤 Wilson Or
- 👤 Venkatesh Narayanan
- 👤 Subramaniam Mohan
- 👤 Peter G. Wohlmut
- 👤 Ramanujam Srinivasan
- 👤 Bobby R. Hunt
- 👤 Thomas W. Ryan
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-04 18:55
CPC Classifications
G; G07; G07C; G07C9/00; G07C9/20; G07C9/22; G07C9/25; G06; G06F; G06F18/00; G06F18/20; G06F18/21; G06V; G06V10/00; G06V10/40; G06V10/42; G06V10/431; G06V30/00; G06V30/10; G06V30/19; G06V30/192; G06V30/194; G06V40/00; G06V40/10; G06V40/16; G06V40/168
Claims Analysis
Total Claims: 10
Claim 1
Independent Claim
230 words
A pattern recognition system, comprising: first means for receiving a plurality of images and corresponding image identification information; second means for processing said plurality of images into image information; third means for determining the most distinctive aspects of said image information, said third means including means for performing an In-Class to Out-of-Class study including: means for generating an In-Class Variation Matrix; means for generating an Out-Class Variation Matrix; means for normalizing said In-Class Variation Matrix; means for normalizing said Out-Class Variation Matrix; means for generating a feature Matrix; means for normalizing said feature Matrix into a normalized feature Matrix; means for partitioninq said normalized feature Matrix into bricks; means for prioritizing said bricks, and means for creating a feature template vector whose elements correspond to a subset of said bricks; fourth means for forming feature vectors of the magnitudes of said most distinctive aspects of said image information; fifth means for storing said feature vectors, said fifth means including neural network processor means adapted to store said feature vectors; sixth means for receiving a query image; seventh means for generating a query feature vector from said query image; eighth means for querying said fifth means to determine the most similar previously stored feature vectors to said query feature vector; ninth means for outputting image identification information corresponding to the previously stored feature vector most similar to said query feature vector.
Claim 2
Dependent Claim
230 words
A pattern recognition system, comprising: first means for receiving a plurality of images and corresponding image identification information; second means for processing said plurality of images into image information; third means for determining the most distinctive aspects of said image information, said third means including means for performing an In-Class to Out-of-Class study including: means for generating an In-Class Variation Matrix; means for generating an Out-Class Variation Matrix; means for normalizing said In-Class Variation Matrix; means for normalizing said Out-Class Variation Matrix; means for generating a feature Matrix; means for normalizing said feature Matrix into a normalized feature Matrix; means for partitioninq said normalized feature Matrix into bricks; means for prioritizing said bricks, and means for creating a feature template vector whose elements correspond to a subset of said bricks; fourth means for forming feature vectors of the magnitudes of said most distinctive aspects of said image information; fifth means for storing said feature vectors, said fifth means including neural network processor means adapted to store said feature vectors; sixth means for receiving a query image; seventh means for generating a query feature vector from said query image; eighth means for querying said fifth means to determine the most similar previously stored feature vectors to said query feature vector; ninth means for outputting image identification information corresponding to the previously stored feature vector most similar to said query feature vector.
Claim 3
Dependent Claim
31 words
A pattern recognition system according to claim 1 further comprising means for outputting a signal indicative of whether any of said plurality of images are sufficiently similar to said query image.
Claim 4
Dependent Claim
49 words
A pattern recognition system according to claim 1 wherein said second means includes means for obtaining a two-dimensional linear transform of said plurality of images and further comprising means for outputting a signal indicative of whether any of said plurality of images are sufficiently similar to said query image.
Claim 5
Independent Claim
272 words
A device for recognizing authorized persons, comprising: first means for receiving at least one image and corresponding image identification information from each of a plurality of authorized persons; second means for processing the images received by said first means into image information; third means for determining the most distinctive aspects of said image information, said third means including means for performing an In-Class to Out-of-Class study including: means for generating an In-Class Variation Matrix; means for generating an Out-Class Variation Matrix; means for normalizing said In-Class Variation Matrix; means for normalizing said Out-Class Variation Matrix; means for generating a feature Matrix; means for normalizing said feature Matrix into a normalized feature Matrix; means for partitioning said normalized feature Matrix into bricks; means for prioritizing said bricks, and means for creating a feature template vector whose elements correspond to a subset of said bricks; fourth means for forming feature vectors of the magnitudes of said most distinctive aspects of said image information; fifth means for storing said feature vectors, said fifth means including neural network processor means adapted to store said feature vectors; sixth means for receiving a query image of a potentially authorized person; means for applying said query image to said fourth means to form a query feature vector corresponding to said query image; seventh means for querying said fifth means to determine whether said query image is within a predetermined range of similarity to any of the images of said plurality of authorized persons; eighth means, responsive to said seventh means, for outputting a signal indicative of whether said potentially authorized person is one of said plurality of authorized persons.
Claim 6
Independent Claim
272 words
A device for recognizing authorized persons, comprising: first means for receiving at least one image and corresponding image identification information from each of a plurality of authorized persons; second means for processing the images received by said first means into image information; third means for determining the most distinctive aspects of said image information, said third means including means for performing an In-Class to Out-of-Class study including: means for generating an In-Class Variation Matrix; means for generating an Out-Class Variation Matrix; means for normalizing said In-Class Variation Matrix; means for normalizing said Out-Class Variation Matrix; means for generating a feature Matrix; means for normalizing said feature Matrix into a normalized feature Matrix; means for partitioning said normalized feature Matrix into bricks; means for prioritizing said bricks, and means for creating a feature template vector whose elements correspond to a subset of said bricks; fourth means for forming feature vectors of the magnitudes of said most distinctive aspects of said image information; fifth means for storing said feature vectors, said fifth means including neural network processor means adapted to store said feature vectors; sixth means for receiving a query image of a potentially authorized person; means for applying said query image to said fourth means to form a query feature vector corresponding to said query image; seventh means for querying said fifth means to determine whether said query image is within a predetermined range of similarity to any of the images of said plurality of authorized persons; eighth means, responsive to said seventh means, for outputting a signal indicative of whether said potentially authorized person is one of said plurality of authorized persons.
Claim 7
Independent Claim
307 words
A device for recognizing the simultaneous presence of a plurality of authorized persons, comprising: first means for receiving at least one image and corresponding image identification information from each of a plurality of authorized persons; second means for processing said at least one image received by said first means into image information; third means for determining the most distinctive aspects of said image information, said third means including means for performing an In-Class to Out-of-Class study including: means for generating an In-Class Variation Matrix; means for generating an Out-Class Variation Matrix; means for normalizing said In-Class Variation Matrix; means for normalizing said Out-Class Variation Matrix; means for generating a feature Matrix; means for normalizing said feature Matrix into a normalized feature Matrix; means for partitioning said normalized feature Matrix into bricks; means for prioritizing said bricks, and means for creating a feature template vector whose elements correspond to a subset of said bricks; fourth means for forming feature vectors of the magnitudes of said most distinctive aspects of said image information; fifth means for storing said feature vectors, said fifth means including neural network processor means adapted to store said feature vectors; sixth means for receiving a query image from each of a predetermined number of potentially authorized persons, said predetermined number being two or more; seventh means for applying said two or more query images received by said sixth means to said fourth means to form feature vectors corresponding to said two or more query images; eighth means for querying said fifth means to determine whether said two or more query images are within a predetermined range of similarity to any of the images of said plurality of authorized persons; ninth means, responsive to said eighth means, for outputting a signal indicative of whether all of said two or more potentially authorized persons are authorized persons.
Claim 8
Independent Claim
307 words
A device for recognizing the simultaneous presence of a plurality of authorized persons, comprising: first means for receiving at least one image and corresponding image identification information from each of a plurality of authorized persons; second means for processing said at least one image received by said first means into image information; third means for determining the most distinctive aspects of said image information, said third means including means for performing an In-Class to Out-of-Class study including: means for generating an In-Class Variation Matrix; means for generating an Out-Class Variation Matrix; means for normalizing said In-Class Variation Matrix; means for normalizing said Out-Class Variation Matrix; means for generating a feature Matrix; means for normalizing said feature Matrix into a normalized feature Matrix; means for partitioning said normalized feature Matrix into bricks; means for prioritizing said bricks, and means for creating a feature template vector whose elements correspond to a subset of said bricks; fourth means for forming feature vectors of the magnitudes of said most distinctive aspects of said image information; fifth means for storing said feature vectors, said fifth means including neural network processor means adapted to store said feature vectors; sixth means for receiving a query image from each of a predetermined number of potentially authorized persons, said predetermined number being two or more; seventh means for applying said two or more query images received by said sixth means to said fourth means to form feature vectors corresponding to said two or more query images; eighth means for querying said fifth means to determine whether said two or more query images are within a predetermined range of similarity to any of the images of said plurality of authorized persons; ninth means, responsive to said eighth means, for outputting a signal indicative of whether all of said two or more potentially authorized persons are authorized persons.
Claim 9
Dependent Claim
226 words
A pattern recognition system, comprising: first means for receiving a plurality of target images and corresponding target image identification information; second means for processing said plurality of target images into image information; third means for determining the most distinctive aspects of said image information, said third means including means for performing an In-Class to Out-of-Class study including: means for generating an In-Class Variation Matrix; means for generating an Out-Class Variation Matrix; means for normalizing said In-Class Variation Matrix; means for normalizing said Out-Class Variation Matrix; means for generating a feature Matrix; means for normalizing said feature Matrix into a normalized feature Matrix; means for partitioning said normalized feature Matrix into bricks; means for prioritizing said bricks, and means for creating a feature template vector whose elements correspond to a subset of said bricks; fourth means for forming feature vectors of the magnitudes of said most distinctive aspects of said image information; fifth means for storing said feature vectors, said fifth means including neural network processor means adapted to store said feature vectors; sixth means for receiving a query image; seventh means for generating a query feature vector from said query image; eighth means for querying said fifth means to determine if any previously stored feature vectors are within a predetermined range of similarity to said query feature vector and for generating a signal indicative of such.
Claim 10
Dependent Claim
226 words
A pattern recognition system, comprising: first means for receiving a plurality of target images and corresponding target image identification information; second means for processing said plurality of target images into image information; third means for determining the most distinctive aspects of said image information, said third means including means for performing an In-Class to Out-of-Class study including: means for generating an In-Class Variation Matrix; means for generating an Out-Class Variation Matrix; means for normalizing said In-Class Variation Matrix; means for normalizing said Out-Class Variation Matrix; means for generating a feature Matrix; means for normalizing said feature Matrix into a normalized feature Matrix; means for partitioning said normalized feature Matrix into bricks; means for prioritizing said bricks, and means for creating a feature template vector whose elements correspond to a subset of said bricks; fourth means for forming feature vectors of the magnitudes of said most distinctive aspects of said image information; fifth means for storing said feature vectors, said fifth means including neural network processor means adapted to store said feature vectors; sixth means for receiving a query image; seventh means for generating a query feature vector from said query image; eighth means for querying said fifth means to determine if any previously stored feature vectors are within a predetermined range of similarity to said query feature vector and for generating a signal indicative of such.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1998-11-06 | 1999-05-08 | 1999-11-07 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2002-11-06 | 2003-05-08 | 2003-11-07 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2006-11-06 | 2007-05-08 | 2007-11-07 | $7700.00 | Window Closed / Lapsed |