Binary optical neural network classifiers for pattern recognition

• US 20040042650A1
• Filed: 08/30/2002
• Published: 03/04/2004
• Est. Priority Date: 08/30/2002
• Status: Abandoned Application

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: Reitz,, Elliott D. II, Ii, David L., Tillotson, Dennis A.
• Application Number:
  US10/231,853
• Publication Number:
  US 20040042650A1
• Time in Patent Office: Days
• US Class Current: 382/158
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection

Abstract

The present invention recites a method and computer program product for determining if an input pattern is a member of an associated class. Data is extracted from a plurality of preselected features within the input pattern, and a numerical feature value for each feature is determined from the extracted feature data. A contribution value for each feature value is calculated via a common transfer function. Predetermined weights are applied to each of the contribution values. The weighted contribution values from the plurality of features are summed, and a mathematical function is applied to the sum of the contribution values to determine a classification result.

Claims

1. A method for determining if an input pattern is a member of an associated class, comprising:

extracting data from a plurality of preselected features within the input pattern;

determining a numerical feature value for each feature from the extracted feature data;

calculating a contribution value for each feature value via a common transfer function;

applying predetermined weights to each of the contribution values;

summing the weighted contribution values from the plurality of features; and

applying a mathematical function to the sum of the contribution values to determine a binary classification result.

2. A method as set forth in claim 1, wherein the common transfer function includes an impulse function, such that a contribution value takes on a value of one when an associated feature value is within a predetermined range and takes on a value of zero when the associated feature value falls outside the predetermined range.

3. A method as set forth in claim 1, wherein the common transfer function includes a radial distance function, such that the value of the function is equal to the absolute value of the difference between the feature value and a calculated mean feature value divided by a calculated standard deviation.

4. A method as set forth in claim 1, wherein the input pattern is a scanned image.

5. A method as set forth in claim 4, wherein the associated class represents a variety of postal indicia.

6. A method as set forth in claim 4, wherein the associated class represents an alphanumeric character.

7. A computer program product operative in a data processing system for use in determining if an input pattern is a member of an associated class, said computer program product comprising:

a feature extraction stage that extracts data from a plurality of preselected features within the input pattern and determines a numerical feature value for each feature from the extracted feature data;

a hidden layer that calculates a contribution value for each feature value via a common transfer function and applies predetermined weights to each of the contribution values; and

an output layer that sums the weighted contribution values from the plurality of features and applies a mathematical function to the sum of the contribution values to determine a binary classification result.

8. A computer program product as set forth in claim 7, wherein the common transfer function in the hidden layer includes an impulse function, such that a contribution value takes on a value of one when an associated feature value is within a predetermined range and takes on a value of zero when the associated feature value falls outside the predetermined range.

9. A computer program product as set forth in claim 7, wherein the common transfer function in the hidden layer includes a radial basis function, such that the value of the function is equal to the absolute value of the difference between the feature value and a calculated mean feature value divided by a calculated standard deviation.

10. A computer program product as set forth in claim 7, wherein the input pattern is a scanned image.

11. A computer program product as set forth in claim 10, wherein the associated class represents a variety of postal indicia.

12. A computer program product as set forth in claim 10, wherein the associated class represents an alphanumeric character.