Probability density function estimation
First Claim
Patent Images
1. A method for classifying an anomaly in a digital image, the method comprising:
- receiving training data comprising a training feature value for each of a plurality of anomaly classification features for each of a plurality of training cases;
defining a neighborhood size for each of a plurality of representation points in feature space for each anomaly classification feature based on the training data;
receiving measured data comprising a measured feature value at an evaluation point in feature space for each anomaly classification feature for a measured case;
determining a scale parameter vector for at least some of the representation points near the evaluation point for each anomaly classification feature to define a respective neighborhood size for that anomaly classification feature;
determining a weight factor for the at least some of the representation points using the respective scale parameter vector; and
applying the weight factor for the at least some of the representation points to the training data at the plurality of representation points to generate a classification probability for the anomaly for the measured case at the evaluation point, wherein the scale parameter vector for a respective anomaly classification feature indicates the respective neighborhood size used to generate the classification probability at the evaluation point in feature space.
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Abstract
A PDF estimator for determining a probability that a detected object is a specific type of object is provided. Training data from a known set is used to functionally describe the relevant neighborhood for specific representation points. The neighborhood is selected based on the measured features of the object to be classified and weights are calculated to be applied to the representation points. A probability is determined based upon the stored training data, the measured features of the object to be classified, and the weights.
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Citations
16 Claims
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1. A method for classifying an anomaly in a digital image, the method comprising:
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receiving training data comprising a training feature value for each of a plurality of anomaly classification features for each of a plurality of training cases; defining a neighborhood size for each of a plurality of representation points in feature space for each anomaly classification feature based on the training data; receiving measured data comprising a measured feature value at an evaluation point in feature space for each anomaly classification feature for a measured case; determining a scale parameter vector for at least some of the representation points near the evaluation point for each anomaly classification feature to define a respective neighborhood size for that anomaly classification feature; determining a weight factor for the at least some of the representation points using the respective scale parameter vector; and applying the weight factor for the at least some of the representation points to the training data at the plurality of representation points to generate a classification probability for the anomaly for the measured case at the evaluation point, wherein the scale parameter vector for a respective anomaly classification feature indicates the respective neighborhood size used to generate the classification probability at the evaluation point in feature space. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A system for classifying anomalies in a digital image, the system comprising:
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a neighborhood definition unit receiving training data comprising a training feature value for each of a plurality of anomaly classification features for each of a plurality of training cases, and defining a neighborhood size for each of a plurality of representation points in feature space based on the training data; a neighborhood determination unit receiving measured data comprising a measured feature value at an evaluation point in feature space for each anomaly classification feature for a measured case, and determining a scale parameter vector for at least some of the representation points near the evaluation point for each anomaly classification feature to define a respective neighborhood size for that anomaly classification feature; a weight determination unit determining a weight factor for the at least some of the representation points using the respective scale parameter vector; and a local estimator applying the weight factor for the at least some of the representation points to the training data at the plurality of representation points to generate a probability density function (PDF) estimate for an anomaly at the evaluation point, wherein the scale parameter vector for a respective anomaly classification feature indicates a respective neighborhood size used to generate the PDF estimate at the evaluation point in feature space. - View Dependent Claims (8, 9, 10, 11)
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12. A computer program product for classifying anomalies, the computer program product having a non-transitory computer-readable medium with a computer program embodied thereon, the computer program comprising:
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computer program code for receiving training data comprising a training feature value for each of a plurality of anomaly classification features for each of a plurality of training cases; computer program code for defining a neighborhood size for each of a plurality of representation points in feature space for each anomaly classification feature based on the training data; computer program code for receiving measured data comprising a measured feature value at an evaluation point in feature space for a measured case; computer program code for determining a scale parameter vector for at least some of the representation points near the evaluation point for each anomaly classification feature to define a respective neighborhood size for that anomaly classification feature; computer program code for determining a weight factor for the at least some of the representation points using the respective scale parameter vector; and computer program code for applying the weight factor for the at least some of the representation points to the training data at the plurality of representation points to generate a probability density function (PDF) estimate for an anomaly for the measured case at the evaluation point, wherein the scale parameter vector for a respective anomaly classification feature indicates a respective neighborhood size used to generate the PDF estimate at the evaluation point in feature space. - View Dependent Claims (13, 14, 15, 16)
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Specification