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US5048100A
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Self organizing neural network method and system for general classification of patterns
Assignee: EMC Corp
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Filing: 1988-12-15
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Granted: 1991-09-10
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Expires: 2008-12-15
Abstract
Abstract A neural network system and method that can adaptively recognize each of many pattern configurations from a set. The system learns and maintains accurate associations between signal pattern configurations and pattern classes with training from a teaching mechanism. The classifying system consists of a distributed input processor and an adaptive association processor. The input processor decomposes an input pattern into modules of localized contextual elements. These elements in turn are mapped onto pattern classes using a self-organizing associative neural scheme. The associative mapping determines which pattern class best represents the input pattern. The computation is done through gating elements that correspond to the contextual elements. Learning is achieved by modifying the gating elements from a true/false response to the computed probabilities for all classes in the set. The system is a parallel and fault tolerant process. It can easily be extended to accommodate an arbitrary number of patterns at an arbitrary degree of precision. The classifier can be applied to automated recognition and inspection of many different types of signals and patterns.
Inventors
- 👤 Michael Kuperstein
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-26 03:10
CPC Classifications
G; G06; G06N; G06N3/00; G06N3/02; G06V; G06V30/00; G06V30/10; G06V30/18; G06V30/1801; G06V30/18019; G06V30/18029; G06V30/18038; G06V30/18048; G06V30/18057
Claims Analysis
Total Claims: 28
Claim 1
Independent Claim
123 words
A method of classifying a pattern into one of a plurality of pattern classes, comprising the steps of: converting the pattern to be classified into an image comprising at least one spatial distribution of intensity amplitudes relative to a spatial window on the pattern; convolving the image with a plurality of different convolution kernels; averaging convolutions of the image across spatial sectors within the spatial window; transforming the averaged convolutions of the image with a distribution of weights for each of the plurality of pattern classes into computed correlations for each of the plurality of pattern classes; and determining a single pattern class of the plurality of pattern classes from the computed correlations, whereby the pattern is classified in said single pattern class.
Claim 2
Independent Claim
123 words
A method of classifying a pattern into one of a plurality of pattern classes, comprising the steps of: converting the pattern to be classified into an image comprising at least one spatial distribution of intensity amplitudes relative to a spatial window on the pattern; convolving the image with a plurality of different convolution kernels; averaging convolutions of the image across spatial sectors within the spatial window; transforming the averaged convolutions of the image with a distribution of weights for each of the plurality of pattern classes into computed correlations for each of the plurality of pattern classes; and determining a single pattern class of the plurality of pattern classes from the computed correlations, whereby the pattern is classified in said single pattern class.
Claim 3
Dependent Claim
12 words
The method according to claim 1, wherein the spatial sectors are overlapping.
Claim 4
Dependent Claim
19 words
The method according to claim 1, further comprising normalizing averaged convolutions of the image before the step of transforming.
Claim 5
Dependent Claim
35 words
The method according to claim 1, wherein the step of convolving the image with the plurality of different convolution kernels comprises using kernels of different configurations in different sizes and orientations relative to the image.
Claim 6
Dependent Claim
103 words
The method according to claim 1, further comprising defining the distribution of weights for a given pattern class of the plurality of pattern classes by storing a map of initial neural weights, initially converging a given pattern known to be classified in the given pattern class into an image comprising a given spatial distribution of intensity amplitudes, transforming the given spatial distribution with the map of initial neural weights into computed correlations for each of the plurality of pattern classes, determining errors from the computed correlations and expected correlations and updating the stored map of initial neural weights depending upon the determined errors.
Claim 7
Dependent Claim
125 words
The method according to claim 4, further comprising defining he distribution of weights for a given pattern class of the plurality of pattern classes and for a given convolution kernel of the plurality of convolution kernels by storing a map of initial neural weights for the given convolution kernel, initially converting a given pattern known to be classified in the given pattern class with the given convolution kernel into an image comprising a given spatial distribution of intensity amplitudes, transforming the given spatial distribution with the map of initial neural weights into computed correlations for each of the plurality of pattern classes, determining errors from the computed correlations and expected correlations and updating the stored map of initial neural weights depending upon the determined errors.
Claim 8
Dependent Claim
22 words
The method according to claim 1, wherein the step of converting comprises converting the pattern to be classified into a binary image.
Claim 9
Dependent Claim
23 words
The method according to claim 1, wherein the step of converting comprises converting the pattern to be classified into a two dimensional image.
Claim 10
Independent Claim
132 words
A method of classifying a pattern into one of a plurality of pattern classes, comprising the steps of: converting the pattern to be classified into an image comprising at least one spatial distribution of intensity amplitudes relative to a spatial window on the pattern by disposing a center of the spatial window at a horizontal center and at a vertical center of the pattern to be classified and totally enclosing the pattern to be classified in the spatial window; transforming the image with a distribution of weights for each of the plurality of pattern classes into computed correlations for each of the plurality of pattern classes; and determining a single pattern class of the plurality of pattern classes from the computed correlations, whereby the pattern is classified in said single pattern class.
Claim 11
Independent Claim
132 words
A method of classifying a pattern into one of a plurality of pattern classes, comprising the steps of: converting the pattern to be classified into an image comprising at least one spatial distribution of intensity amplitudes relative to a spatial window on the pattern by disposing a center of the spatial window at a horizontal center and at a vertical center of the pattern to be classified and totally enclosing the pattern to be classified in the spatial window; transforming the image with a distribution of weights for each of the plurality of pattern classes into computed correlations for each of the plurality of pattern classes; and determining a single pattern class of the plurality of pattern classes from the computed correlations, whereby the pattern is classified in said single pattern class.
Claim 12
Dependent Claim
52 words
The method according to claim 9, for use in character recognition, wherein the step of converting the pattern to be classified into the image comprises converting a page into a binary image, processing the binary image to determine a number of lines of characters thereon and isolating each character on each line.
Claim 13
Dependent Claim
57 words
The method according to claim 10, wherein the step of disposing the spatial window at the horizontal center and vertical center of the pattern comprises disposing the spatial window at the horizontal center of a given character of a given line and at a vertical center of an average height of all characters in the given line.
Claim 14
Dependent Claim
25 words
The method according to claim 1, wherein the step of determining comprising selecting the single pattern class with a highest probability from the computed correlations.
Claim 15
Independent Claim
130 words
A system for classifying a pattern into one of a plurality of pattern classes, comprising: means for converting a pattern to be classified into an image comprising at least one spatial distribution of intensity amplitudes relative to a spatial window on the pattern; means for convolving the image with a plurality of different convolution kernels; means for averaging convolutions of the image across spatial sectors within the spatial window; means for transforming the averaged convolutions of the image with a distribution of weights for each of the plurality of pattern classes into computed correlations for each of the plurality of pattern classes; and means for determining a single pattern class of the plurality of pattern classes from the computed correlations, whereby the pattern is classified in said single pattern class.
Claim 16
Independent Claim
130 words
A system for classifying a pattern into one of a plurality of pattern classes, comprising: means for converting a pattern to be classified into an image comprising at least one spatial distribution of intensity amplitudes relative to a spatial window on the pattern; means for convolving the image with a plurality of different convolution kernels; means for averaging convolutions of the image across spatial sectors within the spatial window; means for transforming the averaged convolutions of the image with a distribution of weights for each of the plurality of pattern classes into computed correlations for each of the plurality of pattern classes; and means for determining a single pattern class of the plurality of pattern classes from the computed correlations, whereby the pattern is classified in said single pattern class.
Claim 17
Dependent Claim
12 words
The system according to claim 13, wherein the spatial sectors are overlapping.
Claim 18
Dependent Claim
23 words
The system according to claim 13, further comprising means for normalizing averaged convolutions of the image before transforming by the means for transforming.
Claim 19
Dependent Claim
27 words
The system according to claim 13, wherein the plurality of different convolution kernels comprises kernels of different configurations in different sizes and orientations relative to the image.
Claim 20
Dependent Claim
115 words
The system according to claim 13, further comprising means for defining the distribution of weights for a given pattern class of the plurality of pattern classes comprising means for storing a map of initial neural weights, means for initially converting a given pattern known to be classified in the given pattern class into a image comprising a given spatial distribution of intensity amplitudes, means for transforming the given spatial distribution with the map of initial neural weights into computed correlations for each of the plurality of pattern classes, means for determining errors from the computed correlations and expected correlations and means for updating the stored map of initial neural weights depending upon the determined errors.
Claim 21
Dependent Claim
132 words
The system according to claim 16, further comprising means for defining the distribution of weights for a given pattern class of the plurality of pattern classes and for a given convolution kernel of the plurality of convolution kernels comprising means for storing a map of initial neural weights for the given convolution kernel, means for initially converting a given pattern known to be classified in the given pattern class with the given convolution kernel into an image comprising a given spatial distribution of, intensity amplitudes, means for transforming the given spatial distribution with the map of initial neural weights into computed correlations for each of the plurality of pattern classes, means for determining errors from the computed correlations and expected correlations and means for updating the stored map of initial neural weights.
Claim 22
Dependent Claim
24 words
The system according to claim 13, wherein the means for converting comprises means for converting the pattern to be classified into a binary image.
Claim 23
Dependent Claim
24 words
The system according to claim 13, wherein the means for converting comprises means for converting the pattern to be classified into a two-dimensional image.
Claim 24
Dependent Claim
27 words
The system according to claim 13, wherein the means for determining comprises means for selecting the single pattern class with a highest probability from the computed correlations.
Claim 25
Independent Claim
142 words
A system for classifying a pattern into one of a plurality of pattern classes, comprising: means for converting a pattern to be classified into an image comprising at least one spatial distribution of intensity amplitudes relative to a spatial window on the pattern, the means for converting comprising means for disposing a center of the spatial window at a horizontal center and at a vertical center of the pattern to be classified and for totally enclosing the pattern to be classified in the spatial window; means for transforming the image with a distribution of weights for each of the plurality of pattern classes into computed correlations for each of the plurality of pattern classes; and means for determining a single pattern class of the plurality of pattern classes from the computed correlations, whereby the pattern is classified in said single pattern class.
Claim 26
Independent Claim
142 words
A system for classifying a pattern into one of a plurality of pattern classes, comprising: means for converting a pattern to be classified into an image comprising at least one spatial distribution of intensity amplitudes relative to a spatial window on the pattern, the means for converting comprising means for disposing a center of the spatial window at a horizontal center and at a vertical center of the pattern to be classified and for totally enclosing the pattern to be classified in the spatial window; means for transforming the image with a distribution of weights for each of the plurality of pattern classes into computed correlations for each of the plurality of pattern classes; and means for determining a single pattern class of the plurality of pattern classes from the computed correlations, whereby the pattern is classified in said single pattern class.
Claim 27
Dependent Claim
57 words
The system according to claim 22, for use in character recognition, wherein the means for converting the pattern to be classified into the image comprises means for converting a page into a binary image, means for processing the binary image to determine a number of lines of characters and means for isolating each character on each line.
Claim 28
Dependent Claim
56 words
The system according to claim 23, wherein the means disposing the spatial window at a horizontal and vertical center of the pattern comprises means disposing the spatial window at the horizontal center of a given character of a given line and at a vertical center of an average height of all characters in the given line.
References Cited (Backward Citations)
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1994-09-09 | 1995-03-11 | 1995-09-10 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 1998-09-09 | 1999-03-11 | 1999-09-10 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2002-09-09 | 2003-03-11 | 2003-09-10 | $7700.00 | Window Closed / Lapsed |