Abstract: To improve the scheduling and tasking of sensors, the present disclosure describes an improved planning system and method for the allocation and management of sensors. In one embodiment, the planning system uses a branch and bound approach of tasking sensors using a heuristic to expedite arrival at a deterministic solution. In another embodiment, a progressive lower bound is applied to the branch and bound approach. Also, in another embodiment, a hybrid branch and bound approach is used where both local and global planning are employed in a tiered fashion.

Abstract: Methods and apparatus for controlling deployment of systems according to various aspects of the present invention operate in conjunction with at least one deployable asset, a scoring system, and an optimization system. The scoring and optimization systems provide an optimized solution to mission planning, asset positioning, and control of multiple entities within a geographic region. The system combines a scoring method with an optimization method to quickly select a globally optimal solution from a larger set of potential solutions.

Abstract: Methods and apparatus for controlling deployment of systems according to various aspects of the present invention operate in conjunction with at least one deployable asset, a scoring system, and an optimization system. The scoring and optimization systems provide an optimized solution to mission planning, asset positioning, and control of multiple entities within a geographic region. The system combines a scoring method with an optimization method to quickly select a globally optimal solution from a larger set of potential solutions.

Abstract: To improve the scheduling and tasking of resources, the present disclosure describes an improved planning system and method for the allocation and management of resources. The planning system uses a branch and bound approach of tasking resources using a heuristic to expedite arrival at a deterministic solution. For each possible functional mode of the resources, an upper bound is determined. The upper bounds are employed in an objective function for the branch and bound process to determine the functional mode in which to place the resources and to determine movement paths for the resources, all in an environment where a hostile force may attempt to destroy the resources.

Abstract: To improve the scheduling and tasking of resources, the present disclosure describes an improved planning system and method for the allocation and management of resources. The planning system uses a branch and bound approach of tasking resources using a heuristic to expedite arrival at a deterministic solution. For each possible functional mode of the resources, an upper bound is determined. The upper bounds are employed in an objective function for the branch and bound process to determine the functional mode in which to place the resources and to determine movement paths for the resources, all in an environment where a hostile force may attempt to destroy the resources.

Abstract: To improve the scheduling and tasking of sensors, the present disclosure describes an improved planning system and method for the allocation and management of sensors. In one embodiment, the planning system uses a branch and bound approach of tasking sensors using a heuristic to expedite arrival at a deterministic solution. In another embodiment, a progressive lower bound is applied to the branch and bound approach. Also, in another embodiment, a hybrid branch and bound approach is used where both local and global planning are employed in a tiered fashion.

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.

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.

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: 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: 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.

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 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: 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: 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.

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: 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 system and method to binarize the bar code image in the presence of noise in order to render bar codes more detectable in the presence of noise. The method of this invention includes the steps of applying 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. A system that implements the method is also described.

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 method and computer program product are disclosed for determining an efficient set of features and an optimal confidence threshold value for a pattern recognition system with at least one output class. An initial set of features is selected based upon an optimization algorithm. A plurality of pattern samples are then classified using the selected feature set. A threshold confidence value is optimized as to maximize the accuracy of the classification. The selected feature set and threshold confidence value are accepted if a cost function based upon classification accuracy meets a predetermined threshold cost function value. The feature set is changed, by adding, removing or replacing a feature within the set based upon the optimization algorithm, if the cost function does not meet the predetermined threshold cost function value.