{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:SJQXBAMZQCJAQNXWTWQX3UQTTM","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":"1b3b976e1da1394d55c829571bf7e804af9d1422e44f9bb0d7cf56dd4d60f1b6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-03T08:24:43Z","title_canon_sha256":"e469841acb2125066cc99bd740e6d455c91bedadfd00900634eff9968369fb10"},"schema_version":"1.0","source":{"id":"1809.00488","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.00488","created_at":"2026-05-18T00:06:35Z"},{"alias_kind":"arxiv_version","alias_value":"1809.00488v1","created_at":"2026-05-18T00:06:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.00488","created_at":"2026-05-18T00:06:35Z"},{"alias_kind":"pith_short_12","alias_value":"SJQXBAMZQCJA","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SJQXBAMZQCJAQNXW","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SJQXBAMZ","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:6066fed92291d3d99355fffda966b91a83e71bef64e064c4db9eb1c85ae3c672","target":"graph","created_at":"2026-05-18T00:06:35Z","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"},"paper":{"abstract_excerpt":"In this paper, we propose an efficient pseudo-marginal Markov chain Monte Carlo (MCMC) sampling approach to draw samples from posterior shape distributions for image segmentation. The computation time of the proposed approach is independent from the size of the training set used to learn the shape prior distribution nonparametrically. Therefore, it scales well for very large data sets. Our approach is able to characterize the posterior probability density in the space of shapes through its samples, and to return multiple solutions, potentially from different modes of a multimodal probability d","authors_text":"Ertunc Erdil, Mujdat Cetin, Sinan Yildirim, Tolga Tasdizen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-03T08:24:43Z","title":"Image Segmentation with Pseudo-marginal MCMC Sampling and Nonparametric Shape Priors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.00488","kind":"arxiv","version":1},"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":"sha256:68fea1c570bd77ad91e994b44b3aad8c69591874164baa8a2c142b5bd339f5b4","target":"record","created_at":"2026-05-18T00:06:35Z","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":"1b3b976e1da1394d55c829571bf7e804af9d1422e44f9bb0d7cf56dd4d60f1b6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-03T08:24:43Z","title_canon_sha256":"e469841acb2125066cc99bd740e6d455c91bedadfd00900634eff9968369fb10"},"schema_version":"1.0","source":{"id":"1809.00488","kind":"arxiv","version":1}},"canonical_sha256":"926170819980920836f69da17dd2139b1529cfb66b272d21fc885435736447a8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"926170819980920836f69da17dd2139b1529cfb66b272d21fc885435736447a8","first_computed_at":"2026-05-18T00:06:35.119777Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:35.119777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"n2GhyZUPhz5E1aKcnTMfDWrWb4ueDwwvnTEghAqdmrMwegNoVzyLHeL7Z3dVkFMrMV2tkyglOjZj4cOm34t3AQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:35.120266Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.00488","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:68fea1c570bd77ad91e994b44b3aad8c69591874164baa8a2c142b5bd339f5b4","sha256:6066fed92291d3d99355fffda966b91a83e71bef64e064c4db9eb1c85ae3c672"],"state_sha256":"93a7d3042d7e857dee7b668ea13e5f35f2c10262ea617874c9ebf8ddb1993142"}