{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:LMN5C2IVO4HGOG7S7V42WWP6MA","short_pith_number":"pith:LMN5C2IV","schema_version":"1.0","canonical_sha256":"5b1bd16915770e671bf2fd79ab59fe601c281d70ef30c709ed690a10724137c5","source":{"kind":"arxiv","id":"2203.15943","version":1},"attestation_state":"computed","paper":{"title":"Self-Supervised Leaf Segmentation under Complex Lighting Conditions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Abbas Kouzani, Adam Guskic, Chang-Tsun Li, Egan Doeven, Lawrence Webb, Ligang He, Michael Vernon, Richard Jiang, Scott Adams, Todd Mcclellan, Xufeng Lin, Yongjian Hu","submitted_at":"2022-03-29T22:59:02Z","abstract_excerpt":"As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging as an effective alternative to various computer vision tasks, its adaptation for image-based plant phenotyping remains rather unexplored. In this work, we present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. The self-supervised semantic segmentation model groups the se"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2203.15943","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-03-29T22:59:02Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"423c194e67fc3237ca44b1d0e718ad2672c7564fc88f2c31a398e806bd7505af","abstract_canon_sha256":"693935a2e1b2e04f321caeb3cf336ef255076d6f39b20c55932308e635486ac3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:10:05.375977Z","signature_b64":"77tXQb0iFm+/d35V9zd+9zUp1UkVTX37JkveEcIzXA3cilb6Sie8lFpWSb/Mk06pUDROFEicr5Sm/6WNJK8RAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b1bd16915770e671bf2fd79ab59fe601c281d70ef30c709ed690a10724137c5","last_reissued_at":"2026-07-05T04:10:05.375558Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:10:05.375558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Leaf Segmentation under Complex Lighting Conditions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Abbas Kouzani, Adam Guskic, Chang-Tsun Li, Egan Doeven, Lawrence Webb, Ligang He, Michael Vernon, Richard Jiang, Scott Adams, Todd Mcclellan, Xufeng Lin, Yongjian Hu","submitted_at":"2022-03-29T22:59:02Z","abstract_excerpt":"As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging as an effective alternative to various computer vision tasks, its adaptation for image-based plant phenotyping remains rather unexplored. In this work, we present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. The self-supervised semantic segmentation model groups the se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.15943","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2203.15943/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2203.15943","created_at":"2026-07-05T04:10:05.375635+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.15943v1","created_at":"2026-07-05T04:10:05.375635+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.15943","created_at":"2026-07-05T04:10:05.375635+00:00"},{"alias_kind":"pith_short_12","alias_value":"LMN5C2IVO4HG","created_at":"2026-07-05T04:10:05.375635+00:00"},{"alias_kind":"pith_short_16","alias_value":"LMN5C2IVO4HGOG7S","created_at":"2026-07-05T04:10:05.375635+00:00"},{"alias_kind":"pith_short_8","alias_value":"LMN5C2IV","created_at":"2026-07-05T04:10:05.375635+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LMN5C2IVO4HGOG7S7V42WWP6MA","json":"https://pith.science/pith/LMN5C2IVO4HGOG7S7V42WWP6MA.json","graph_json":"https://pith.science/api/pith-number/LMN5C2IVO4HGOG7S7V42WWP6MA/graph.json","events_json":"https://pith.science/api/pith-number/LMN5C2IVO4HGOG7S7V42WWP6MA/events.json","paper":"https://pith.science/paper/LMN5C2IV"},"agent_actions":{"view_html":"https://pith.science/pith/LMN5C2IVO4HGOG7S7V42WWP6MA","download_json":"https://pith.science/pith/LMN5C2IVO4HGOG7S7V42WWP6MA.json","view_paper":"https://pith.science/paper/LMN5C2IV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.15943&json=true","fetch_graph":"https://pith.science/api/pith-number/LMN5C2IVO4HGOG7S7V42WWP6MA/graph.json","fetch_events":"https://pith.science/api/pith-number/LMN5C2IVO4HGOG7S7V42WWP6MA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LMN5C2IVO4HGOG7S7V42WWP6MA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LMN5C2IVO4HGOG7S7V42WWP6MA/action/storage_attestation","attest_author":"https://pith.science/pith/LMN5C2IVO4HGOG7S7V42WWP6MA/action/author_attestation","sign_citation":"https://pith.science/pith/LMN5C2IVO4HGOG7S7V42WWP6MA/action/citation_signature","submit_replication":"https://pith.science/pith/LMN5C2IVO4HGOG7S7V42WWP6MA/action/replication_record"}},"created_at":"2026-07-05T04:10:05.375635+00:00","updated_at":"2026-07-05T04:10:05.375635+00:00"}