{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:MMGTMHIXG3S5N6AZGCUUDS2RWH","short_pith_number":"pith:MMGTMHIX","schema_version":"1.0","canonical_sha256":"630d361d1736e5d6f81930a941cb51b1f13ae80f5a09b6c81ea445c78c75e3bb","source":{"kind":"arxiv","id":"1203.4855","version":1},"attestation_state":"computed","paper":{"title":"Texture Classification Approach Based on Combination of Edge & Co-occurrence and Local Binary Pattern","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Shervan Fekri Ershad","submitted_at":"2012-03-21T23:33:30Z","abstract_excerpt":"Texture classification is one of the problems which has been paid much attention on by computer scientists since late 90s. If texture classification is done correctly and accurately, it can be used in many cases such as Pattern recognition, object tracking, and shape recognition. So far, there have been so many methods offered to solve this problem. Near all these methods have tried to extract and define features to separate different labels of textures really well. This article has offered an approach which has an overall process on the images of textures based on Local binary pattern and Gra"},"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":"1203.4855","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"cs.CV","submitted_at":"2012-03-21T23:33:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"759141a8ab64b68d929583e782935983656e41ddb4b4c7031373735e602d1305","abstract_canon_sha256":"f393e1e741860ecd9836e4b3834898f5a27b96cb364c670280a7ff62cb7c54bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:59:28.584536Z","signature_b64":"KmOxPmY9OoM2Ef79HLvUa4jrf9gdRw/hJjLJFmUIRE4eyDC7w+CYU0d1wbdIWHh3LcQHX7F8MakSU7Zo9IuGDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"630d361d1736e5d6f81930a941cb51b1f13ae80f5a09b6c81ea445c78c75e3bb","last_reissued_at":"2026-05-18T03:59:28.583898Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:59:28.583898Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Texture Classification Approach Based on Combination of Edge & Co-occurrence and Local Binary Pattern","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Shervan Fekri Ershad","submitted_at":"2012-03-21T23:33:30Z","abstract_excerpt":"Texture classification is one of the problems which has been paid much attention on by computer scientists since late 90s. If texture classification is done correctly and accurately, it can be used in many cases such as Pattern recognition, object tracking, and shape recognition. So far, there have been so many methods offered to solve this problem. Near all these methods have tried to extract and define features to separate different labels of textures really well. This article has offered an approach which has an overall process on the images of textures based on Local binary pattern and Gra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1203.4855","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":"1203.4855","created_at":"2026-05-18T03:59:28.583993+00:00"},{"alias_kind":"arxiv_version","alias_value":"1203.4855v1","created_at":"2026-05-18T03:59:28.583993+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1203.4855","created_at":"2026-05-18T03:59:28.583993+00:00"},{"alias_kind":"pith_short_12","alias_value":"MMGTMHIXG3S5","created_at":"2026-05-18T12:27:14.488303+00:00"},{"alias_kind":"pith_short_16","alias_value":"MMGTMHIXG3S5N6AZ","created_at":"2026-05-18T12:27:14.488303+00:00"},{"alias_kind":"pith_short_8","alias_value":"MMGTMHIX","created_at":"2026-05-18T12:27:14.488303+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/MMGTMHIXG3S5N6AZGCUUDS2RWH","json":"https://pith.science/pith/MMGTMHIXG3S5N6AZGCUUDS2RWH.json","graph_json":"https://pith.science/api/pith-number/MMGTMHIXG3S5N6AZGCUUDS2RWH/graph.json","events_json":"https://pith.science/api/pith-number/MMGTMHIXG3S5N6AZGCUUDS2RWH/events.json","paper":"https://pith.science/paper/MMGTMHIX"},"agent_actions":{"view_html":"https://pith.science/pith/MMGTMHIXG3S5N6AZGCUUDS2RWH","download_json":"https://pith.science/pith/MMGTMHIXG3S5N6AZGCUUDS2RWH.json","view_paper":"https://pith.science/paper/MMGTMHIX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1203.4855&json=true","fetch_graph":"https://pith.science/api/pith-number/MMGTMHIXG3S5N6AZGCUUDS2RWH/graph.json","fetch_events":"https://pith.science/api/pith-number/MMGTMHIXG3S5N6AZGCUUDS2RWH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MMGTMHIXG3S5N6AZGCUUDS2RWH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MMGTMHIXG3S5N6AZGCUUDS2RWH/action/storage_attestation","attest_author":"https://pith.science/pith/MMGTMHIXG3S5N6AZGCUUDS2RWH/action/author_attestation","sign_citation":"https://pith.science/pith/MMGTMHIXG3S5N6AZGCUUDS2RWH/action/citation_signature","submit_replication":"https://pith.science/pith/MMGTMHIXG3S5N6AZGCUUDS2RWH/action/replication_record"}},"created_at":"2026-05-18T03:59:28.583993+00:00","updated_at":"2026-05-18T03:59:28.583993+00:00"}