Abstract: 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.

Abstract: The systems and methods of this invention provide a simple classifier suitable for real time use. The classifier adapts to changing system conditions and does not require recalculating the details of the classifier.

Abstract: A simple and robust method for determining the orientation angle of a scanned image or the angular orientation of an object in an electronic image based on using descriptors of the image and systems that implement the method are disclosed. A system including means for acquiring the electronic image, means for determining image centroid coordinates, means for obtaining second order moments corresponding to the image, and, means for determining an orientation angle of a principal axis of the image implements the method.

Abstract: 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.

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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In order to render information contained in images more detectable in the presence of noise, the present invention discloses a system and method to binarize the images in the presence of noise. The method of this invention comprises the steps of applying a dynamic range reducing filter to the digitized image values, obtaining a range reduced image; applying an edge detecting filter to the range reduced image, obtaining a filtered image; and then, adaptively binarizing the digitized image utilizing the corresponding filtered image to obtain an adaptive threshold.

Abstract: In order to render bar codes more detectable in the presence of noise, the present invention discloses a system and method to binarize the bar code image in the presence of noise. The method of this invention comprises the steps of applying of a gradient detecting filter to the bar code image, obtaining a filtered image, and then, adaptively binarizing the bar code image utilizing the corresponding filtered image to obtain an adaptive threshold.

Abstract: A method and system of reading images. The method includes decimating an image and normalizing the decimated image. Then, rebinning grayscale pixel data associated with the decimated image and reassigning a selected range of grayscales representing a region of interest of the remapped grayscale pixel data to one while zeroing others. The method also includes labeling the connected pixels associated with the range of grayscales representing the region of interest and then extending the connected pixels through region growing using a preselected offset value. Lastly, the method includes filtering segmented regions of the connected pixels to eliminate certain candidates. The system is designed to implement the steps of the enumerated method.

Abstract: A system and method that enables early detection of hazardous materials, such as explosives and biological materials, in the early phases of mail handling or processing, is disclosed. The early detection of such hazardous material utilizing the system and method of this invention can be performed while the mail is being processed. The system includes at least two primary elements: an activation sub-system and a sense and analyze sub-system.