AUTOMATIC DESIGN OF MORPHOLOGICAL ALGORITHMS FOR MACHINE VISION
First Claim
1. A method for automated selection of a parameterized operator sequence to achieve a pattern classification task, comprising the steps of:
- inputting a collection of labeled data patterns;
deriving statistical descriptions of the inputted labeled data patterns;
determining the criterion function which will be used to derive the classifier performance;
determining classifier performance for each of a plurality of candidate operator sequences and corresponding parameter values, using the derived statistical descriptions;
identifying an optimal classifier performance among the determined classifier performances according to specified criteria; and
selecting the operator sequence and corresponding parameter values, associated with the identified optimal classifier performance.
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Abstract
The present invention provides a technique for automated selection of a parameterized operator sequence to achieve a pattern classification task. A collection of labeled data patterns is input and statistical descriptions of the inputted labeled data patterns are then derived. Classifier performance for each of a plurality of candidate operator/parameter sequences is determined. The optimal classifier performance among the candidate classifier performances is then identified. Performance metric information, including, for example, the selected operator sequence/parameter combination, will be outputted. The operator sequences selected can be chosen from a default set of operators, or may be a user-defined set. The operator sequences may include any morphological operators, such as, erosion, dilation, closing, opening, close-open, and open-close.
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Citations
21 Claims
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1. A method for automated selection of a parameterized operator sequence to achieve a pattern classification task, comprising the steps of:
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inputting a collection of labeled data patterns; deriving statistical descriptions of the inputted labeled data patterns; determining the criterion function which will be used to derive the classifier performance; determining classifier performance for each of a plurality of candidate operator sequences and corresponding parameter values, using the derived statistical descriptions; identifying an optimal classifier performance among the determined classifier performances according to specified criteria; and selecting the operator sequence and corresponding parameter values, associated with the identified optimal classifier performance. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A computer-readable medium readable by a machine, tangibly embodying a program of instructions executable on the machine to perform method steps for automated selection of a parameterized operator sequence to achieve a pattern classification task, the method steps comprising:
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inputting a collection of labeled data patterns; deriving statistical descriptions of the inputted labeled data patterns; determining the criterion function which will be used to derive the classifier performance; determining classifier performance for each of a plurality of candidate operator sequences and corresponding parameter values, using the derived statistical descriptions; identifying an optimal classifier performance among the determined classifier performances according to specified criteria; and selecting the operator sequence and corresponding parameter values, associated with the identified optimal classifier performance.
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20. A method for automated selection of a parameterized operator sequence to a achieve a pattern classification task, comprising the steps of:
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inputting a collection of labeled data patterns; deriving a statistical model of the inputted labeled data patterns; determining classifier performances for each of a plurality of candidate operator sequences from the derived statistical model; identifying an optimal classifier performance among the determined classifier performances according to specified criteria; and selecting the operator sequence and corresponding parameter values associated with the identified optimal classifier performance. - View Dependent Claims (21)
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Specification