{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ZUTFHPHCGAD35DC3MBPVS3ZLWE","short_pith_number":"pith:ZUTFHPHC","schema_version":"1.0","canonical_sha256":"cd2653bce23007be8c5b605f596f2bb13f13db1a3c06cb15e2146228e78a72be","source":{"kind":"arxiv","id":"1710.10522","version":1},"attestation_state":"computed","paper":{"title":"Object Recognition by Using Multi-level Feature Point Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Timeo Dubois, Yang Cheng","submitted_at":"2017-10-28T19:54:21Z","abstract_excerpt":"In this paper, we present a novel approach for object recognition in real-time by employing multilevel feature analysis and demonstrate the practicality of adapting feature extraction into a Naive Bayesian classification framework that enables simple, efficient, and robust performance. We also show the proposed method scales well as the number of level-classes grows. To effectively understand the patches surrounding a keypoint, the trained classifier uses hundreds of simple binary features and models class posterior probabilities. In addition, the classification process is computationally chea"},"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":"1710.10522","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-28T19:54:21Z","cross_cats_sorted":[],"title_canon_sha256":"2d1f76bb2687fd3a1a9829acd48829bb46871f694e9ed33700059feeccf1c090","abstract_canon_sha256":"0dcda4041b59ffdd19d6496d6fd91835ec27c4d0f324663c524633e4c836d917"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:49.299659Z","signature_b64":"fotwjcTWHMpiGgJ/qShhz3mp65dtpxlqVAGBIbcjrdbV1VkT4VUs/FStC5xcvuYQHp3MKHMzrsv/XkTbRXz2CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd2653bce23007be8c5b605f596f2bb13f13db1a3c06cb15e2146228e78a72be","last_reissued_at":"2026-05-18T00:31:49.299009Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:49.299009Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Object Recognition by Using Multi-level Feature Point Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Timeo Dubois, Yang Cheng","submitted_at":"2017-10-28T19:54:21Z","abstract_excerpt":"In this paper, we present a novel approach for object recognition in real-time by employing multilevel feature analysis and demonstrate the practicality of adapting feature extraction into a Naive Bayesian classification framework that enables simple, efficient, and robust performance. We also show the proposed method scales well as the number of level-classes grows. To effectively understand the patches surrounding a keypoint, the trained classifier uses hundreds of simple binary features and models class posterior probabilities. In addition, the classification process is computationally chea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10522","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":"1710.10522","created_at":"2026-05-18T00:31:49.299106+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.10522v1","created_at":"2026-05-18T00:31:49.299106+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10522","created_at":"2026-05-18T00:31:49.299106+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZUTFHPHCGAD3","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZUTFHPHCGAD35DC3","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZUTFHPHC","created_at":"2026-05-18T12:31:59.375834+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/ZUTFHPHCGAD35DC3MBPVS3ZLWE","json":"https://pith.science/pith/ZUTFHPHCGAD35DC3MBPVS3ZLWE.json","graph_json":"https://pith.science/api/pith-number/ZUTFHPHCGAD35DC3MBPVS3ZLWE/graph.json","events_json":"https://pith.science/api/pith-number/ZUTFHPHCGAD35DC3MBPVS3ZLWE/events.json","paper":"https://pith.science/paper/ZUTFHPHC"},"agent_actions":{"view_html":"https://pith.science/pith/ZUTFHPHCGAD35DC3MBPVS3ZLWE","download_json":"https://pith.science/pith/ZUTFHPHCGAD35DC3MBPVS3ZLWE.json","view_paper":"https://pith.science/paper/ZUTFHPHC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.10522&json=true","fetch_graph":"https://pith.science/api/pith-number/ZUTFHPHCGAD35DC3MBPVS3ZLWE/graph.json","fetch_events":"https://pith.science/api/pith-number/ZUTFHPHCGAD35DC3MBPVS3ZLWE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZUTFHPHCGAD35DC3MBPVS3ZLWE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZUTFHPHCGAD35DC3MBPVS3ZLWE/action/storage_attestation","attest_author":"https://pith.science/pith/ZUTFHPHCGAD35DC3MBPVS3ZLWE/action/author_attestation","sign_citation":"https://pith.science/pith/ZUTFHPHCGAD35DC3MBPVS3ZLWE/action/citation_signature","submit_replication":"https://pith.science/pith/ZUTFHPHCGAD35DC3MBPVS3ZLWE/action/replication_record"}},"created_at":"2026-05-18T00:31:49.299106+00:00","updated_at":"2026-05-18T00:31:49.299106+00:00"}