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• W2159439347 abstract "Segmentation of vegetation is a critical step in using machine vision for field automation tasks. A new method calledreduced-dimension clustering (RDC) was developed based on theoretical considerations about the color distribution of fieldimages. RDC performed unsupervised classification of pixels in field images into vegetation and background classes. Bayesclassifiers were then trained and used for vegetation segmentation. The performance of the classifiers trained using the RDCmethod was compared with that of other segmentation methods. The RDC method produced segmentation performance that wasconsistently high, with average segmentation success rates of 89.6% and 91.9% across both cloudy and sunny lighting conditions,respectively. Statistical analyses of segmentation performance coupled with three-dimensional visualization of classifierdecision surfaces produced insight into why classifier performance varied across the methods. These results should lead to improvementsin segmentation methods for field images acquired under variable lighting conditions." @default.
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• W2159439347 date "2004-01-01" @default.
• W2159439347 modified "2023-09-23" @default.
• W2159439347 title "REDUCED-DIMENSION CLUSTERING FOR VEGETATION SEGMENTATION" @default.
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• W2159439347 doi "https://doi.org/10.13031/2013.16024" @default.
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