Leaf Vein Network CNN Results
DOI10.5281/zenodo.4008361Zenodo4008361MaRDI QIDQ6695237
Dataset published at Zenodo repository.
Author name not available (Why is that?)
Publication date: 30 August 2020
Copyright license: No records found.
Results for leaf vein networks extracted using the LeafVeinCNN software package. The original image data set is available from Blonder et al. (2019)https://doi.org/10.1002/ecy.2844. The LeafVeinCNN software used in the analysis is available at https://doi.org/10.5281/zenodo.4007730 The Results_xxx.zip files containall the Excel results spreadsheets separated by the code for each field site. results.xls provides a summary of all the network metrics for each file that was analysable Results_figures.pdf provides a summary image of the processing steps and results for each leaf segment Network_images.pdf contains a colour-coded image of each network superimposed on the leaf segment HLD_plots shows the binary tree following Hierarchical Network Decomposition PR_results.zip contains the Excel spreadsheets for evaluation of different enhancement methods for each leaf segment. PR_summary.xls provides a summary of the performance of each enhancement method. PR_F1_images.pdf and PR_FBeta2_images.pdf show the pixel classification for each enhancement and segmentation methodcompared to the manual ground-truth using two different optimum criteria (F1 and FBeta2). PR_fullwidth_plots show the full Precision-Recall plots for the full-width binary image compared to the manual ground-truth using the FBeta2 metric. PR_skeleton_plots show the full Precision-Recall plots for the skeletonised binary imagecompared to the manual ground-truthusing the FBeta2 metric. PR_threshold_plots.pdf show how a set of network metrics vary with the segmentation threshold for each enhancement method.
This page was built for dataset: Leaf Vein Network CNN Results
