  Automatic detection and segmentation of overlapping leaves in dense foliage
can be a difficult task, particularly for leaves with strong textures and high
occlusions. We present Dense-Leaves, an image dataset with ground truth
segmentation labels that can be used to train and quantify algorithms for leaf
segmentation in the wild. We also propose a pyramid convolutional neural
network with multi-scale predictions that detects and discriminates leaf
boundaries from interior textures. Using these detected boundaries,
closed-contour boundaries around individual leaves are estimated with a
watershed-based algorithm. The result is an instance segmenter for dense
leaves. Promising segmentation results for leaves in dense foliage are
obtained.
