{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-pyramid-cnn-for-dense-leaves-segmentation","title":"A Pyramid CNN for Dense-Leaves Segmentation","arxiv_id":"1804.01646","date":"2018-04-05","proceeding":null,"authors":["Daniel D. Morris"],"abstract":"Automatic detection and segmentation of overlapping leaves in dense foliage\ncan be a difficult task, particularly for leaves with strong textures and high\nocclusions. We present Dense-Leaves, an image dataset with ground truth\nsegmentation labels that can be used to train and quantify algorithms for leaf\nsegmentation in the wild. We also propose a pyramid convolutional neural\nnetwork with multi-scale predictions that detects and discriminates leaf\nboundaries from interior textures. Using these detected boundaries,\nclosed-contour boundaries around individual leaves are estimated with a\nwatershed-based algorithm. The result is an instance segmenter for dense\nleaves. Promising segmentation results for leaves in dense foliage are\nobtained.","url_abs":"http://arxiv.org/abs/1804.01646v1","url_pdf":"http://arxiv.org/pdf/1804.01646v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-pyramid-cnn-for-dense-leaves-segmentation","repo_url":"https://github.com/NickLucche/pyramid-cnn-leaves-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.01646","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}