{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:OA3QUETVTQMVOTR7NRLPGBMMMH","short_pith_number":"pith:OA3QUETV","schema_version":"1.0","canonical_sha256":"70370a12759c19574e3f6c56f3058c61e5dd800ce30017befc2e77086af573b0","source":{"kind":"arxiv","id":"1212.4527","version":1},"attestation_state":"computed","paper":{"title":"GMM-Based Hidden Markov Random Field for Color Image and 3D Volume Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Quan Wang","submitted_at":"2012-12-18T22:30:23Z","abstract_excerpt":"In this project, we first study the Gaussian-based hidden Markov random field (HMRF) model and its expectation-maximization (EM) algorithm. Then we generalize it to Gaussian mixture model-based hidden Markov random field. The algorithm is implemented in MATLAB. We also apply this algorithm to color image segmentation problems and 3D volume segmentation problems."},"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":"1212.4527","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2012-12-18T22:30:23Z","cross_cats_sorted":[],"title_canon_sha256":"3a6ee3634926e2ff4b70354b228f04da85211942d04c8ef44cf9402c4a15d880","abstract_canon_sha256":"50582be2df36322bea54589ca43b98abcbaafc0f2c06735e363a448b5718f7a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:38:12.104925Z","signature_b64":"Oxwy9LTCRRVUC6KgbGE9CWTy5JM7aV7qu2t3ieBCZCGAIez3wkCfCTwnZt1uBTCkvlDesSq4bDBDvWruF2h+Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70370a12759c19574e3f6c56f3058c61e5dd800ce30017befc2e77086af573b0","last_reissued_at":"2026-05-18T03:38:12.104192Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:38:12.104192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GMM-Based Hidden Markov Random Field for Color Image and 3D Volume Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Quan Wang","submitted_at":"2012-12-18T22:30:23Z","abstract_excerpt":"In this project, we first study the Gaussian-based hidden Markov random field (HMRF) model and its expectation-maximization (EM) algorithm. Then we generalize it to Gaussian mixture model-based hidden Markov random field. The algorithm is implemented in MATLAB. We also apply this algorithm to color image segmentation problems and 3D volume segmentation problems."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.4527","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":""},"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":"1212.4527","created_at":"2026-05-18T03:38:12.104308+00:00"},{"alias_kind":"arxiv_version","alias_value":"1212.4527v1","created_at":"2026-05-18T03:38:12.104308+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1212.4527","created_at":"2026-05-18T03:38:12.104308+00:00"},{"alias_kind":"pith_short_12","alias_value":"OA3QUETVTQMV","created_at":"2026-05-18T12:27:16.716162+00:00"},{"alias_kind":"pith_short_16","alias_value":"OA3QUETVTQMVOTR7","created_at":"2026-05-18T12:27:16.716162+00:00"},{"alias_kind":"pith_short_8","alias_value":"OA3QUETV","created_at":"2026-05-18T12:27:16.716162+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/OA3QUETVTQMVOTR7NRLPGBMMMH","json":"https://pith.science/pith/OA3QUETVTQMVOTR7NRLPGBMMMH.json","graph_json":"https://pith.science/api/pith-number/OA3QUETVTQMVOTR7NRLPGBMMMH/graph.json","events_json":"https://pith.science/api/pith-number/OA3QUETVTQMVOTR7NRLPGBMMMH/events.json","paper":"https://pith.science/paper/OA3QUETV"},"agent_actions":{"view_html":"https://pith.science/pith/OA3QUETVTQMVOTR7NRLPGBMMMH","download_json":"https://pith.science/pith/OA3QUETVTQMVOTR7NRLPGBMMMH.json","view_paper":"https://pith.science/paper/OA3QUETV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1212.4527&json=true","fetch_graph":"https://pith.science/api/pith-number/OA3QUETVTQMVOTR7NRLPGBMMMH/graph.json","fetch_events":"https://pith.science/api/pith-number/OA3QUETVTQMVOTR7NRLPGBMMMH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OA3QUETVTQMVOTR7NRLPGBMMMH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OA3QUETVTQMVOTR7NRLPGBMMMH/action/storage_attestation","attest_author":"https://pith.science/pith/OA3QUETVTQMVOTR7NRLPGBMMMH/action/author_attestation","sign_citation":"https://pith.science/pith/OA3QUETVTQMVOTR7NRLPGBMMMH/action/citation_signature","submit_replication":"https://pith.science/pith/OA3QUETVTQMVOTR7NRLPGBMMMH/action/replication_record"}},"created_at":"2026-05-18T03:38:12.104308+00:00","updated_at":"2026-05-18T03:38:12.104308+00:00"}