{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:JJKUT5T7J6XLKPIUZURIBO7V7L","short_pith_number":"pith:JJKUT5T7","schema_version":"1.0","canonical_sha256":"4a5549f67f4faeb53d14cd2280bbf5fadcf98cbe8ec1ed2ed56f3ccdedc7e0b3","source":{"kind":"arxiv","id":"1501.00752","version":2},"attestation_state":"computed","paper":{"title":"A Deep-structured Conditional Random Field Model for Object Silhouette Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Alexander Wong, Mohammad Shafiee, Zohreh Azimifar","submitted_at":"2015-01-05T03:09:34Z","abstract_excerpt":"In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interactions between adjacent state layers are established by inter-layer connectivity dynamically determined based on inter-frame optical flow. By incorporate both spatial and temporal context in a dynamic fashion within such a deep-structured probabilistic graphical model, the proposed"},"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":"1501.00752","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-01-05T03:09:34Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"a9b3e84482d03e7b0ce226c6387843cb2413cf318b7e0de1a5e0f53c324b1169","abstract_canon_sha256":"a9ad220971b9c25b748ef6db529a7eee69db4da11308ab198a9d068c00217ac4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:36.990435Z","signature_b64":"gU4xE6c5GRL+86UrOUFO6MLo1Z9df4yxc9lpPgnd3S5uWfBKgvx7hIGqYtW8VZ58QDNq7QgpNB2WEz6+KZruCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a5549f67f4faeb53d14cd2280bbf5fadcf98cbe8ec1ed2ed56f3ccdedc7e0b3","last_reissued_at":"2026-05-18T01:20:36.990038Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:36.990038Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Deep-structured Conditional Random Field Model for Object Silhouette Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Alexander Wong, Mohammad Shafiee, Zohreh Azimifar","submitted_at":"2015-01-05T03:09:34Z","abstract_excerpt":"In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interactions between adjacent state layers are established by inter-layer connectivity dynamically determined based on inter-frame optical flow. By incorporate both spatial and temporal context in a dynamic fashion within such a deep-structured probabilistic graphical model, the proposed"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.00752","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":""},"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":"1501.00752","created_at":"2026-05-18T01:20:36.990096+00:00"},{"alias_kind":"arxiv_version","alias_value":"1501.00752v2","created_at":"2026-05-18T01:20:36.990096+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.00752","created_at":"2026-05-18T01:20:36.990096+00:00"},{"alias_kind":"pith_short_12","alias_value":"JJKUT5T7J6XL","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_16","alias_value":"JJKUT5T7J6XLKPIU","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_8","alias_value":"JJKUT5T7","created_at":"2026-05-18T12:29:27.538025+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/JJKUT5T7J6XLKPIUZURIBO7V7L","json":"https://pith.science/pith/JJKUT5T7J6XLKPIUZURIBO7V7L.json","graph_json":"https://pith.science/api/pith-number/JJKUT5T7J6XLKPIUZURIBO7V7L/graph.json","events_json":"https://pith.science/api/pith-number/JJKUT5T7J6XLKPIUZURIBO7V7L/events.json","paper":"https://pith.science/paper/JJKUT5T7"},"agent_actions":{"view_html":"https://pith.science/pith/JJKUT5T7J6XLKPIUZURIBO7V7L","download_json":"https://pith.science/pith/JJKUT5T7J6XLKPIUZURIBO7V7L.json","view_paper":"https://pith.science/paper/JJKUT5T7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1501.00752&json=true","fetch_graph":"https://pith.science/api/pith-number/JJKUT5T7J6XLKPIUZURIBO7V7L/graph.json","fetch_events":"https://pith.science/api/pith-number/JJKUT5T7J6XLKPIUZURIBO7V7L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JJKUT5T7J6XLKPIUZURIBO7V7L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JJKUT5T7J6XLKPIUZURIBO7V7L/action/storage_attestation","attest_author":"https://pith.science/pith/JJKUT5T7J6XLKPIUZURIBO7V7L/action/author_attestation","sign_citation":"https://pith.science/pith/JJKUT5T7J6XLKPIUZURIBO7V7L/action/citation_signature","submit_replication":"https://pith.science/pith/JJKUT5T7J6XLKPIUZURIBO7V7L/action/replication_record"}},"created_at":"2026-05-18T01:20:36.990096+00:00","updated_at":"2026-05-18T01:20:36.990096+00:00"}