{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:W5D6GR65G3YEXGFOEIXBXZDH4U","short_pith_number":"pith:W5D6GR65","schema_version":"1.0","canonical_sha256":"b747e347dd36f04b98ae222e1be467e50b76f3a3d52e4a6b69e725c5af8ba79d","source":{"kind":"arxiv","id":"1102.4816","version":1},"attestation_state":"computed","paper":{"title":"Computationally efficient algorithms for statistical image processing. Implementation in R","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.AP","stat.ME","stat.ML"],"primary_cat":"stat.CO","authors_text":"Mikhail A. Langovoy, Olaf Wittich","submitted_at":"2011-02-23T18:46:56Z","abstract_excerpt":"In the series of our earlier papers on the subject, we proposed a novel statistical hypothesis testing method for detection of objects in noisy images. The method uses results from percolation theory and random graph theory. We developed algorithms that allowed to detect objects of unknown shapes in the presence of nonparametric noise of unknown level and of unknown distribution. No boundary shape constraints were imposed on the objects, only a weak bulk condition for the object's interior was required. Our algorithms have linear complexity and exponential accuracy. In the present paper, we de"},"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":"1102.4816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2011-02-23T18:46:56Z","cross_cats_sorted":["cs.CV","stat.AP","stat.ME","stat.ML"],"title_canon_sha256":"f4b9e106b5686d02fd112f0d3054fba7dca657d9df2361141a8c52409541cf19","abstract_canon_sha256":"99c616a3b2bf924b2e1aace8ec24ae80adf82c38ab0496a9738ff97998ab58aa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:28:07.297829Z","signature_b64":"bEjrH4M2Mghutz6uOj1fbXelFQ1w9c8QgoNh20SpSThFzy0zMU1C/r0YGOV2zZmRGZSzbZHuhf2fhCWgj52gAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b747e347dd36f04b98ae222e1be467e50b76f3a3d52e4a6b69e725c5af8ba79d","last_reissued_at":"2026-05-18T04:28:07.297054Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:28:07.297054Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Computationally efficient algorithms for statistical image processing. Implementation in R","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.AP","stat.ME","stat.ML"],"primary_cat":"stat.CO","authors_text":"Mikhail A. Langovoy, Olaf Wittich","submitted_at":"2011-02-23T18:46:56Z","abstract_excerpt":"In the series of our earlier papers on the subject, we proposed a novel statistical hypothesis testing method for detection of objects in noisy images. The method uses results from percolation theory and random graph theory. We developed algorithms that allowed to detect objects of unknown shapes in the presence of nonparametric noise of unknown level and of unknown distribution. No boundary shape constraints were imposed on the objects, only a weak bulk condition for the object's interior was required. Our algorithms have linear complexity and exponential accuracy. In the present paper, we de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1102.4816","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":"1102.4816","created_at":"2026-05-18T04:28:07.297164+00:00"},{"alias_kind":"arxiv_version","alias_value":"1102.4816v1","created_at":"2026-05-18T04:28:07.297164+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1102.4816","created_at":"2026-05-18T04:28:07.297164+00:00"},{"alias_kind":"pith_short_12","alias_value":"W5D6GR65G3YE","created_at":"2026-05-18T12:26:44.992195+00:00"},{"alias_kind":"pith_short_16","alias_value":"W5D6GR65G3YEXGFO","created_at":"2026-05-18T12:26:44.992195+00:00"},{"alias_kind":"pith_short_8","alias_value":"W5D6GR65","created_at":"2026-05-18T12:26:44.992195+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/W5D6GR65G3YEXGFOEIXBXZDH4U","json":"https://pith.science/pith/W5D6GR65G3YEXGFOEIXBXZDH4U.json","graph_json":"https://pith.science/api/pith-number/W5D6GR65G3YEXGFOEIXBXZDH4U/graph.json","events_json":"https://pith.science/api/pith-number/W5D6GR65G3YEXGFOEIXBXZDH4U/events.json","paper":"https://pith.science/paper/W5D6GR65"},"agent_actions":{"view_html":"https://pith.science/pith/W5D6GR65G3YEXGFOEIXBXZDH4U","download_json":"https://pith.science/pith/W5D6GR65G3YEXGFOEIXBXZDH4U.json","view_paper":"https://pith.science/paper/W5D6GR65","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1102.4816&json=true","fetch_graph":"https://pith.science/api/pith-number/W5D6GR65G3YEXGFOEIXBXZDH4U/graph.json","fetch_events":"https://pith.science/api/pith-number/W5D6GR65G3YEXGFOEIXBXZDH4U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W5D6GR65G3YEXGFOEIXBXZDH4U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W5D6GR65G3YEXGFOEIXBXZDH4U/action/storage_attestation","attest_author":"https://pith.science/pith/W5D6GR65G3YEXGFOEIXBXZDH4U/action/author_attestation","sign_citation":"https://pith.science/pith/W5D6GR65G3YEXGFOEIXBXZDH4U/action/citation_signature","submit_replication":"https://pith.science/pith/W5D6GR65G3YEXGFOEIXBXZDH4U/action/replication_record"}},"created_at":"2026-05-18T04:28:07.297164+00:00","updated_at":"2026-05-18T04:28:07.297164+00:00"}