3-D Histogram-Based Segmentation and Leaf Detection for Rosette Plants
Recognition and segmentation of plant organs like leaves is one of the major challenges in digital plant phenotyping. Here we present a 3-D histogram-based segmentation and recognition approach for top view images of rosette plants such as Arabidopsis thaliana and tobacco. Furthermore a euclidean-distance-map-based method for the detection of leaves and the corresponding plant leaf segmentation method were developed. An approach for the detection of optimal leaf split points for the separation of overlapping leaf segments was created. We tested and tuned our algorithms for the Leaf Segmentation Challenge (LSC). The results demonstrate that our method is robust and handles demanding imaging situations and different species with high accuracy.
Keywords3-D Histogram thresholding Euclidean distance map Graph analysis Leaf counting Leaf segmentation
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