Patent US5555320A successfully refreshed from Google Patents.
US5555320A
Expired / Lapsed
US
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Pattern recognition system with improved recognition rate using nonlinear transformation
Assignee: Toshiba Corp
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Filing: 1993-11-29
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Granted: 1996-09-10
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Expires: 2013-11-29
Abstract
Abstract A pattern recognition system capable of handling the non-Gaussian feature vector distribution in the feature vector space such that the recognition rate can be improved while using the pattern matching based on the assumption of the Gaussian feature vector distribution. In the system, a nonlinear transformation for each recognizable category is applied to the feature vector extracted from the input pattern to be recognized, to obtain a transformed feature data for the input pattern, where the nonlinear transformation maps linearly inseparable distributions in a vector space containing the feature vector onto linearly separable distributions. Then, the transformed feature data for the input pattern is compared with reference feature model for each recognizable category indicating a feature vector distribution for each category, to find a category of the input pattern as that of the reference feature model closest to the transformed feature data for the input pattern.
Inventors
- 👤 Bunpei Irie
- 👤 Yoshiaki Kurosawa
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 15:59
CPC Classifications
G; G06; G06F; G06F18/00; G06F18/20; G06F18/21; G06F18/213
Claims Analysis
Total Claims: 24
Claim 1
Independent Claim
205 words
A pattern recognition system for identifying an input pattern with one of a plurality of prescribed recognizable categories for patterns, comprising: pattern input means for inputting the input pattern to be recognized; feature extraction means for extracting a feature vector from the input pattern inputted by the pattern input means; nonlinear transformation means for applying a nonlinear transformation for each of said prescribed recognizable categories to the feature vector extracted by the feature extraction means to obtain a transformed feature data for the input pattern for each of said prescribed recognizable categories, where the nonlinear transformation for each of said prescribed recognizable categories maps linearly inseparable distributions in a vector space containing the feature vector onto separable distributions; and matching means for comparing the transformed feature data for the input pattern for each of said prescribed recognizable categories with a corresponding reference feature model for each of said prescribed recognizable categories indicating a feature vector distribution for each of said prescribed recognizable categories, for finding a category of the input pattern as that of the reference feature model for one of said prescribed recognizable categories that is closest to the transformed feature data for the input pattern for said one of said prescribed recognizable categories.
Claim 2
Dependent Claim
205 words
A pattern recognition system for identifying an input pattern with one of a plurality of prescribed recognizable categories for patterns, comprising: pattern input means for inputting the input pattern to be recognized; feature extraction means for extracting a feature vector from the input pattern inputted by the pattern input means; nonlinear transformation means for applying a nonlinear transformation for each of said prescribed recognizable categories to the feature vector extracted by the feature extraction means to obtain a transformed feature data for the input pattern for each of said prescribed recognizable categories, where the nonlinear transformation for each of said prescribed recognizable categories maps linearly inseparable distributions in a vector space containing the feature vector onto separable distributions; and matching means for comparing the transformed feature data for the input pattern for each of said prescribed recognizable categories with a corresponding reference feature model for each of said prescribed recognizable categories indicating a feature vector distribution for each of said prescribed recognizable categories, for finding a category of the input pattern as that of the reference feature model for one of said prescribed recognizable categories that is closest to the transformed feature data for the input pattern for said one of said prescribed recognizable categories.
Claim 3
Dependent Claim
24 words
The system of claim 1, wherein the nonlinear transformation maps a nonlinear subset representing a distribution in the vector space onto a linear space.
Claim 4
Dependent Claim
85 words
The system of claim 2, further comprising means for determining the nonlinear transformation for each of said prescribed recognizable categories applied by the nonlinear transformation means, by approximating the feature vector distribution for each of said prescribed recognizable categories obtained by using learning patterns of said each of said prescribed recognizable categories by a nonlinear subset of the feature vector distribution, and choosing a transformation which maps the nonlinear subset onto a linear space as the nonlinear transformation for each of said prescribed recognizable categories.
Claim 5
Dependent Claim
24 words
The system of claim 1, further comprising dictionary means for registering the reference feature model for each of said prescribed recognizable categories in advance.
Claim 6
Dependent Claim
69 words
The system of claim 4, further comprising means for determining the reference feature model for each of said prescribed recognizable categories registered into the dictionary means as data indicating a distribution obtained by applying the nonlinear transformation for each of said prescribed recognizable categories to the feature vector distribution for each of said prescribed recognizable categories obtained by using learning patterns of said each of said prescribed recognizable categories.
Claim 7
Dependent Claim
95 words
The system of claim 1, wherein the matching means compares the transformed feature data for the input pattern with the reference feature model for each of said prescribed recognizable categories, by calculating a similarity of the transformed feature data for the input pattern and the reference feature model for each of said prescribed recognizable categories, such that the reference feature model closest to the transformed feature data for the input pattern is found as that for which the calculated similarity is maximum among all the reference feature model for all of said prescribed recognizable categories.
Claim 8
Dependent Claim
33 words
The system of claim 1, wherein the nonlinear transformation subdivides the feature vector distribution into a number of small regions, and maps each of the subdivided small regions separately onto a linear space.
Claim 9
Dependent Claim
50 words
The system of claim 1, wherein when the feature vector distribution for a certain one of said prescribed recognizable categories contains a plurality of spatially separated distribution blocks in the vector space, the nonlinear transformation superposes said plurality of spatially separated distribution blocks with each other in the vector space.
Claim 10
Dependent Claim
44 words
The system of claim 1, further comprising presentation means for presenting information related to the feature vector distribution for each of said prescribed recognizable categories obtained by using learning patterns of said each of said prescribed recognizable categories for an inspection by an operator.
Claim 11
Dependent Claim
172 words
The system of claim 9, wherein the presentation means presents the information in at least one of: (1) a mode for displaying a projection of the feature vector distribution onto a subspace of the vector space; (2) a mode for displaying a cross section of the feature vector distribution; (3) a mode for displaying a plurality of cross sections of the feature vector distribution in an ordered sequence; (4) a mode for displaying the feature vector distributions for more than one of said prescribed recognizable categories together, with different brightnesses or colors assigned for different ones of said prescribed recognizable categories; (5) a mode for displaying at least one of the input pattern and the feature vector corresponding to a plurality of points in the feature vector distribution, in identical positional relationships as said plurality of points have in the feature vector distribution; and (6) a mode for presenting at least one of the input pattern and the feature vector for a point in the feature vector distribution specified by an operator.
Claim 12
Dependent Claim
32 words
The system of claim 1, wherein the feature extraction means comprises a KxK Gaussian Function Filter, K is an integer, and wherein a KxK-dimensional feature vector is extracted as a result thereof.
Claim 13
Independent Claim
196 words
A method of pattern recognition for identifying an input pattern with one of a plurality of prescribed recognizable categories for patterns, comprising the steps of: (a) inputting the input pattern to be recognized; (b) extracting a feature vector from the input pattern inputted at the step (a); (c) applying a nonlinear transformation for each of said prescribed recognizable categories to the feature vector extracted at the step (b) to obtain a transformed feature data for the input pattern for each of said prescribed recognizable categories, where the nonlinear transformation for each of said prescribed recognizable categories maps linearly inseparable distributions in a vector space containing the feature vector onto linearly separable distributions; and (d) comparing the transformed feature data for the input pattern for each said prescribed recognizable categories with a corresponding reference feature model for each of said prescribed recognizable categories indicating a feature vector distribution for each of said prescribed recognizable categories, for finding a category of the input pattern as that of the reference feature model for one of said prescribed recognizable categories that is closest to the transformed feature data for the input pattern for said one of said prescribed recognizable categories.
Claim 14
Independent Claim
196 words
A method of pattern recognition for identifying an input pattern with one of a plurality of prescribed recognizable categories for patterns, comprising the steps of: (a) inputting the input pattern to be recognized; (b) extracting a feature vector from the input pattern inputted at the step (a); (c) applying a nonlinear transformation for each of said prescribed recognizable categories to the feature vector extracted at the step (b) to obtain a transformed feature data for the input pattern for each of said prescribed recognizable categories, where the nonlinear transformation for each of said prescribed recognizable categories maps linearly inseparable distributions in a vector space containing the feature vector onto linearly separable distributions; and (d) comparing the transformed feature data for the input pattern for each said prescribed recognizable categories with a corresponding reference feature model for each of said prescribed recognizable categories indicating a feature vector distribution for each of said prescribed recognizable categories, for finding a category of the input pattern as that of the reference feature model for one of said prescribed recognizable categories that is closest to the transformed feature data for the input pattern for said one of said prescribed recognizable categories.
Claim 15
Dependent Claim
24 words
The method of claim 12, wherein the nonlinear transformation maps a nonlinear subset representing a distribution in the vector space onto a linear space.
Claim 16
Dependent Claim
85 words
The method of claim 13, further comprising the step of determining the nonlinear transformation for each of said prescribed recognizable categories applied at the step (c), by approximating the feature vector distribution for each of said prescribed recognizable categories obtained by using learning patterns of said each of said prescribed recognizable categories by a nonlinear subset of the feature vector distribution, and choosing a transformation which maps the nonlinear subset onto a linear space as the nonlinear transformation for each of said prescribed recognizable categories.
Claim 17
Dependent Claim
30 words
The method of claim 12, further comprising the step of registering the reference feature model for each of said prescribed recognizable categories in a form of a dictionary in advance.
Claim 18
Dependent Claim
69 words
The method of claim 15, further comprising the step of determining the reference feature model for each of said prescribed recognizable categories registered into the dictionary as data indicating a distribution obtained by applying the nonlinear transformation for each of said prescribed recognizable categories to the feature vector distribution for each of said prescribed recognizable categories obtained by using learning patterns of said each of said prescribed recognizable categories.
Claim 19
Dependent Claim
95 words
The method of claim 12, wherein the step (d) compares the transformed feature data for the input pattern with the reference feature model for each of said prescribed recognizable categories, by calculating a similarity of the transformed feature data for the input pattern and the reference feature model for each of said prescribed recognizable categories, such that the reference feature model closest to the transformed feature data for the input pattern is found as that for which the calculated similarity is maximum among all the reference feature models for all of said prescribed recognizable categories.
Claim 20
Dependent Claim
33 words
The method of claim 12, wherein the nonlinear transformation subdivides the feature vector distribution into a number of small regions, and maps each of the subdivided small regions separately onto a linear space.
Claim 21
Dependent Claim
50 words
The method of claim 12, wherein when the feature vector distribution for a certain one of said prescribed recognizable categories contains a plurality of spatially separated distribution blocks in the vector space, the nonlinear transformation superposes said plurality of spatially separated distribution blocks with each other in the vector space.
Claim 22
Dependent Claim
44 words
The method of claim 12, further comprising the step of presenting information related to the feature vector distribution for each of said prescribed recognizable categories obtained by using learning patterns of said each of said prescribed recognizable categories for an inspection by an operator.
Claim 23
Dependent Claim
172 words
The method of claim 20, wherein the presenting step presents the information in at least one of: (1) a mode for displaying a projection of the feature vector distribution onto a subspace of the vector space: (2) a mode for displaying a cross section of the feature vector distribution: (3) a mode for displaying a plurality of cross sections of the feature vector distribution in an ordered sequence; (4) a mode for displaying the feature vector distributions for more than one of said prescribed recognizable categories together, with different brightnesses or colors assigned for different ones of said prescribed recognizable categories; (5) a mode for displaying at least one of the input pattern and the feature vector corresponding to a plurality of points in the feature vector distribution, in identical positional relationship as said plurality of points have in the feature vector distribution; and (6) a mode for presenting at least one of the input pattern and the feature vector for a point in the feature vector distribution specified by an operator.
Claim 24
Dependent Claim
38 words
The method of claim 12, wherein the feature vector is extracted in the step (b) by using a KxK Gaussian Function Filter, K is an integer, and wherein a KxK-dimensional feature vector is extracted as a result thereof.
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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) | 1999-09-10 | 2000-03-11 | 2000-09-10 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2003-09-10 | 2004-03-11 | 2004-09-10 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2007-09-10 | 2008-03-11 | 2008-09-10 | $7700.00 | Window Closed / Lapsed |