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Fig. 2 | BMC Bioinformatics

Fig. 2

From: Automated interpretation of 3D laserscanned point clouds for plant organ segmentation

Fig. 2

Quantative results showing the F-measure and entropy values as a function of number of clusters. The F-measure results (top row) show a better performance for Algorithm 1 using data mappings (HC) than those for k-means (KM) clustering on normalized histograms directly. This is also captured by the entropy values (middle row), as it considers the distributions of different labels within the clusters. The lower value, the more the clusters are dominated by histograms of a particular label, and therefore the better the clustering. For all methods the algorithm required only few minutes per run and the number of cluster (bottom row)

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