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- W124871032 abstract "Abstract Although several approaches have beenintroduced to automatically identify internal logdefects using computed tomography (CT) imagery,most of these have been feasibility efforts andconsequent y have had several limitations: (1)reports of classification accuracy are largelysubjective, not statistical, (2) there has been noattempt to achieve real-time operation, and (3)texture information has not been used forsegmentation, but has been limited to recognitionprocedures. Neural network classifiers based onlocal neighborhoods have the potential to greatlyincrease computational speed, can be implementedto incorporate textural features duringsegmentation, and can provide an objectiveassessment of classification performance. Thispaper describes a method in which a multilayerfeed-forward network is used to perform pixel-by-pixel defect classification. After initial thresholdingto separate wood from background and internalvoids, the classifier labels each pixel of a CT sliceusing histogram-normalized values of pixels in a3×3×3 window about the classified pixel. A post-processing step then removes some spurious pixelmisclassifications. Our approach is able to identifybark, knots, decay, splits, and clear wood on severalspecies of hardwoods. By using normalized pixelvalues as inputs to the classifier, the neural networkis able to formulate and apply aggregate features,such as average and standard deviation, as well astexture-related features. With appropriatehardware. the method can operate in real time." @default.
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- W124871032 date "1995-01-01" @default.
- W124871032 modified "2023-09-26" @default.
- W124871032 title "Log Defect Recognition Using CT-images and Neural Net Classifiers" @default.
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