{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:TGK5Q6AOO2IMPO5LEXXFMRF7EO","short_pith_number":"pith:TGK5Q6AO","canonical_record":{"source":{"id":"2411.08592","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-13T13:19:51Z","cross_cats_sorted":[],"title_canon_sha256":"035af065bc05d9ba91fb34db7ad117a8309c8fc6bfef020123704ab0817ad27f","abstract_canon_sha256":"b57b92ffb9491fd5b40d18a0956408259eef6e72270df0015ca9dbc0a4d9c934"},"schema_version":"1.0"},"canonical_sha256":"9995d8780e7690c7bbab25ee5644bf23b3f9f9b8e12b79ba979545b9221f1993","source":{"kind":"arxiv","id":"2411.08592","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.08592","created_at":"2026-07-05T10:27:19Z"},{"alias_kind":"arxiv_version","alias_value":"2411.08592v2","created_at":"2026-07-05T10:27:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.08592","created_at":"2026-07-05T10:27:19Z"},{"alias_kind":"pith_short_12","alias_value":"TGK5Q6AOO2IM","created_at":"2026-07-05T10:27:19Z"},{"alias_kind":"pith_short_16","alias_value":"TGK5Q6AOO2IMPO5L","created_at":"2026-07-05T10:27:19Z"},{"alias_kind":"pith_short_8","alias_value":"TGK5Q6AO","created_at":"2026-07-05T10:27:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:TGK5Q6AOO2IMPO5LEXXFMRF7EO","target":"record","payload":{"canonical_record":{"source":{"id":"2411.08592","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-13T13:19:51Z","cross_cats_sorted":[],"title_canon_sha256":"035af065bc05d9ba91fb34db7ad117a8309c8fc6bfef020123704ab0817ad27f","abstract_canon_sha256":"b57b92ffb9491fd5b40d18a0956408259eef6e72270df0015ca9dbc0a4d9c934"},"schema_version":"1.0"},"canonical_sha256":"9995d8780e7690c7bbab25ee5644bf23b3f9f9b8e12b79ba979545b9221f1993","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:27:19.259900Z","signature_b64":"NLgD80WaADs40znQY0kVmMzcCRao1TyWG9GSXlhmN5Qk3/ZAqh7z98xZfqUdZxzlNxayvfdvVm6biJD70zN1BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9995d8780e7690c7bbab25ee5644bf23b3f9f9b8e12b79ba979545b9221f1993","last_reissued_at":"2026-07-05T10:27:19.258846Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:27:19.258846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.08592","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:27:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HSPGSCp047GyvwoOa/0ZFIwYgY/PsSSQXuZYKUgEPC9AylTjzbQ0HqrwrKXkr3rOX0OdVMncv3EiShpB6bcVBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T10:05:57.719606Z"},"content_sha256":"d0e63ac947cacc7cd8c2bf0d9a3d19a357ef2cb6e60a62f23ac467907c36502d","schema_version":"1.0","event_id":"sha256:d0e63ac947cacc7cd8c2bf0d9a3d19a357ef2cb6e60a62f23ac467907c36502d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:TGK5Q6AOO2IMPO5LEXXFMRF7EO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Slender Object Scene Segmentation in Remote Sensing Image Based on Learnable Morphological Skeleton with Segment Anything Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Faqiang Wang, Jun Liu, Jun Xie, Liqiang Zhang, Wenxiao Li, Zhengyang Hou","submitted_at":"2024-11-13T13:19:51Z","abstract_excerpt":"Morphological methods play a crucial role in remote sensing image processing, due to their ability to capture and preserve small structural details. However, most of the existing deep learning models for semantic segmentation are based on the encoder-decoder architecture including U-net and Segment Anything Model (SAM), where the downsampling process tends to discard fine details. In this paper, we propose a new approach that integrates learnable morphological skeleton prior into deep neural networks using the variational method. To address the difficulty in backpropagation in neural networks "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.08592","kind":"arxiv","version":2},"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/2411.08592/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:27:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UcnX3u+sttfa+ODY8OlZuzIs0mlnp0pakdPTS/GOM/M8qgu2j5f5MTXxHfwdm5Ly5FyQNYlWveZBjawf+kRYBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T10:05:57.720123Z"},"content_sha256":"1f094d9294a90ee1424fc7f4d708e1ffe2c5c45a3d8f1968a22ab4550823d079","schema_version":"1.0","event_id":"sha256:1f094d9294a90ee1424fc7f4d708e1ffe2c5c45a3d8f1968a22ab4550823d079"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TGK5Q6AOO2IMPO5LEXXFMRF7EO/bundle.json","state_url":"https://pith.science/pith/TGK5Q6AOO2IMPO5LEXXFMRF7EO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TGK5Q6AOO2IMPO5LEXXFMRF7EO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-21T10:05:57Z","links":{"resolver":"https://pith.science/pith/TGK5Q6AOO2IMPO5LEXXFMRF7EO","bundle":"https://pith.science/pith/TGK5Q6AOO2IMPO5LEXXFMRF7EO/bundle.json","state":"https://pith.science/pith/TGK5Q6AOO2IMPO5LEXXFMRF7EO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TGK5Q6AOO2IMPO5LEXXFMRF7EO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:TGK5Q6AOO2IMPO5LEXXFMRF7EO","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b57b92ffb9491fd5b40d18a0956408259eef6e72270df0015ca9dbc0a4d9c934","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-13T13:19:51Z","title_canon_sha256":"035af065bc05d9ba91fb34db7ad117a8309c8fc6bfef020123704ab0817ad27f"},"schema_version":"1.0","source":{"id":"2411.08592","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.08592","created_at":"2026-07-05T10:27:19Z"},{"alias_kind":"arxiv_version","alias_value":"2411.08592v2","created_at":"2026-07-05T10:27:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.08592","created_at":"2026-07-05T10:27:19Z"},{"alias_kind":"pith_short_12","alias_value":"TGK5Q6AOO2IM","created_at":"2026-07-05T10:27:19Z"},{"alias_kind":"pith_short_16","alias_value":"TGK5Q6AOO2IMPO5L","created_at":"2026-07-05T10:27:19Z"},{"alias_kind":"pith_short_8","alias_value":"TGK5Q6AO","created_at":"2026-07-05T10:27:19Z"}],"graph_snapshots":[{"event_id":"sha256:1f094d9294a90ee1424fc7f4d708e1ffe2c5c45a3d8f1968a22ab4550823d079","target":"graph","created_at":"2026-07-05T10:27:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.08592/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Morphological methods play a crucial role in remote sensing image processing, due to their ability to capture and preserve small structural details. However, most of the existing deep learning models for semantic segmentation are based on the encoder-decoder architecture including U-net and Segment Anything Model (SAM), where the downsampling process tends to discard fine details. In this paper, we propose a new approach that integrates learnable morphological skeleton prior into deep neural networks using the variational method. To address the difficulty in backpropagation in neural networks ","authors_text":"Faqiang Wang, Jun Liu, Jun Xie, Liqiang Zhang, Wenxiao Li, Zhengyang Hou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-13T13:19:51Z","title":"Slender Object Scene Segmentation in Remote Sensing Image Based on Learnable Morphological Skeleton with Segment Anything Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.08592","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d0e63ac947cacc7cd8c2bf0d9a3d19a357ef2cb6e60a62f23ac467907c36502d","target":"record","created_at":"2026-07-05T10:27:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b57b92ffb9491fd5b40d18a0956408259eef6e72270df0015ca9dbc0a4d9c934","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-13T13:19:51Z","title_canon_sha256":"035af065bc05d9ba91fb34db7ad117a8309c8fc6bfef020123704ab0817ad27f"},"schema_version":"1.0","source":{"id":"2411.08592","kind":"arxiv","version":2}},"canonical_sha256":"9995d8780e7690c7bbab25ee5644bf23b3f9f9b8e12b79ba979545b9221f1993","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9995d8780e7690c7bbab25ee5644bf23b3f9f9b8e12b79ba979545b9221f1993","first_computed_at":"2026-07-05T10:27:19.258846Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:27:19.258846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NLgD80WaADs40znQY0kVmMzcCRao1TyWG9GSXlhmN5Qk3/ZAqh7z98xZfqUdZxzlNxayvfdvVm6biJD70zN1BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:27:19.259900Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.08592","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d0e63ac947cacc7cd8c2bf0d9a3d19a357ef2cb6e60a62f23ac467907c36502d","sha256:1f094d9294a90ee1424fc7f4d708e1ffe2c5c45a3d8f1968a22ab4550823d079"],"state_sha256":"b0c783dd684dd5c0803af73fbf6550ea4cd083e912197273e79a860c1e7d4820"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hHAjtbtX/eJrRyd+oVaZZowUVEX6eO7lexhJyOy35eyfvBAQ6UbLEGKoOsyzWMIeDLJ7YoVBA4alffqNz/FBAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T10:05:57.724130Z","bundle_sha256":"6d41a17cc5b69a8e807dd79afee9b651f1c2f15ef0a5f2f0d980d0420518332f"}}