Lockheed Martin Corporation

Elliott D Reitz, Ii

Dennis A Tillotson

David L Ii

Rosemary DeSantis Paradis

Charles Call

Joseph F Cecala

David L Li

Stanley W Driggs

A method and computer program product are disclosed for automatically encoding a complex system architecture for a pattern recognition classifier. A plurality of subclassifier states are defined as a plurality of sets of state variables, each set of variables corresponding to a subclassifier state. A set of rules are then determined for a state machine governing transitions between the plurality of subclassifier states. The plurality of sets of state variables and the determined rules are encoded into a configuration file. This configuration file is provided to a generic classifier system, including a state machine and a predefined generic classifier object.

Automatic encoding of a complex system architecture in a pattern recognition classifier

The present invention recites a method and computer program product for identifying and correcting systematic noise in a pattern recognition classifier. A plurality of input patterns that have been determined not to be associated with any of a set of at least one represented output class are rejected by the pattern recognition classifier. A subset of pattern samples is selected from the rejected input patterns based upon the similarity of each pattern to one of the represented output classes. The selected pattern samples are subjected to an independent review to determine if they were correctly rejected. The classifier is retrained based upon this independent review of the selected pattern samples.

Method and computer program product for identifying and correcting systematic noise in a pattern recognition system

A method and computer program product are disclosed for identifying output classes with multi-modal dispersion in feature space and incorporating multi-modal structure into a pattern recognition system architecture. A plurality of input patterns, determined not to be associated with any of a set of at least one represented output class by a pattern recognition classifier, are rejected. The rejected pattern samples are grouped into clusters according to the similarities between the pattern samples. Clusters that contain samples associated with a represented output class are identified via independent review. The classifier is then retrained to recognize the identified clusters as output pseudoclasses separate from the represented output class with which they are associated. The system architecture is reorganized to incorporate the output pseudoclasses. The output pseudoclasses are rejoined to their associated class after classification.

Method and computer program product for identifying output classes with multi-modal dispersion in feature space and incorporating multi-modal structure into a pattern recognition system

A method and computer program product are disclosed for use in classifying an input pattern into one of a plurality of output classes. A plurality of modular classifiers each represent a set of at least one associated output class. The modular classifiers are capable of being trained separately from the system. The classifiers select one of the associated output classes as a classification result and compute an associated confidence value. This confidence value possesses common significance across the plurality of modular classifiers. A processing stage processes the confidence values from the plurality of modular classifiers. As a result of this processing, the processing stage selects an associated classification result.

Modular classification architecture for a pattern recognition application

The present invention recites a method and computer program product for classifying an input pattern into an associated output class. For each of a plurality of classification rungs, each rung representing at least one output class, the a priori probability that an input pattern will be associated with a class represented by that rung is determined. A sequential order of processing for the plurality of classification rungs is assigned, based upon the a priori probability associated with each classification rung. A confidence value, associated with a represented class, is computed for each classification rung, in the assigned order, until a class is selected or a termination event occurs. The associated confidence values are compared to a predetermined threshold, and a class associated with a confidence value that first exceeds the threshold is selected, if a confidence value exceeds the threshold.

Sequential classifier for use in pattern recognition system

The present invention recites a method and computer program product for identifying one or more new pattern classes and incorporating the classes into a pattern recognition classifier as output classes. A plurality of input patterns determined not to be associated with any of a set of at least one represented output class by a pattern recognition classifier are rejected. The rejected pattern samples are then grouped into clusters according to the similarities between the pattern samples. Clusters that represent new pattern classes are identified via independent review. The classifier is then retrained to include the new pattern classes as output classes.

Method and computer program product for identifying and incorporating new output classes in a pattern recognition system during system operation

The present invention recites a method and computer program product for generating a set of training samples from a single ideal pattern for each output class of a pattern recognition classifier. A system equivalent pattern is generated for each of a plurality of classes from a corresponding ideal pattern. A noise model, simulating at least one type of noise expected in a real-world classifier input pattern, is then applied to each system equivalent pattern a set number times to produce, for each output class, a number of training samples. Each training sample simulates defects expected in real-world classifier input patterns.

Method and computer program product for producing a pattern recognition training set

A method and computer program product are disclosed for generating training data over a plurality of feature variables for a new output class in a pattern recognition classifier representing a plurality of existing classes with previously calculated statistical parameters. Deviation measures are generated for each feature variable for each of the plurality of existing classes from the calculated statistical parameters. The deviation measures for each feature variable are averaged across the plurality of existing classes. Feature data is extracted from an ideal pattern, representing the new class, for each of the feature variables. Statistical parameters are approximated for each feature variable for the new class from the extracted feature data and the averaged deviation measures.

Method and computer program product for generating training data for a new class in a pattern recognition classifier

A method is disclosed for classifying an input pattern into an associated class through use of a compound classifier. Data pertaining to preselected features present within the input pattern are extracted. A discriminant value for each of a plurality of classes is then determined via a first classification technique. This value reflects the relative likelihood that a class is the associated class. The class with the highest relative likelihood is selected. A confidence value is generated via a second classification technique. This confidence value is reflective of the a posteriori probability that the selected class is the associated class. The selected class is rejected if the determined confidence value is below a predetermined threshold value.

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Bradenton, FL, United States of America

Elliott D Reitz, Ii