期刊
2018 15TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV)
卷 -, 期 -, 页码 238-245出版社
IEEE
DOI: 10.1109/CRV.2018.00041
关键词
Leaf segmentation; CNN; Dense foliage; Boundary detection
资金
- MSU startup grant
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.
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