Automatic design of morphological algorithms for machine vision

Details Automatic design of morphological algorithms for machine vision Automatic design of morphological algorithms for machine vision

2007-05-07

2011-12-06

Le, Brian (Department: 2624)

Image analysis

Learning systems

Trainable classifiers or pattern recognizers

C382S160000

Reexamination Certificate

active

08073244

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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