{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:XRPKWD7BVHETQ63HIPE3RN764X","short_pith_number":"pith:XRPKWD7B","schema_version":"1.0","canonical_sha256":"bc5eab0fe1a9c9387b6743c9b8b7fee5dd9b8648e03671061476239d64e7f63d","source":{"kind":"arxiv","id":"2204.09648","version":1},"attestation_state":"computed","paper":{"title":"One-Class Model for Fabric Defect Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Byunghyun Jang, David Troendle, Hao Zhou, Yixin Chen","submitted_at":"2022-04-20T17:46:30Z","abstract_excerpt":"An automated and accurate fabric defect inspection system is in high demand as a replacement for slow, inconsistent, error-prone, and expensive human operators in the textile industry. Previous efforts focused on certain types of fabrics or defects, which is not an ideal solution. In this paper, we propose a novel one-class model that is capable of detecting various defects on different fabric types. Our model takes advantage of a well-designed Gabor filter bank to analyze fabric texture. We then leverage an advanced deep learning algorithm, autoencoder, to learn general feature representation"},"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":"2204.09648","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-20T17:46:30Z","cross_cats_sorted":[],"title_canon_sha256":"04119c6170a5f5115f1d525af85465613b2b75516ce57aea05ec5abe021d777c","abstract_canon_sha256":"088a3b4b2208392620082d02eff1fba3772233a2ce2e5f812b3e38485b4e71ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:16:32.466508Z","signature_b64":"T6Yj+xbPVAIW8OPBx/XEORtz3YG7xfhBR4ku7m69pTNsGCNJyvITMjhBLWQ9fYqXZ6MQBnJlKwIGXHGN8ksdBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc5eab0fe1a9c9387b6743c9b8b7fee5dd9b8648e03671061476239d64e7f63d","last_reissued_at":"2026-07-05T04:16:32.466053Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:16:32.466053Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"One-Class Model for Fabric Defect Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Byunghyun Jang, David Troendle, Hao Zhou, Yixin Chen","submitted_at":"2022-04-20T17:46:30Z","abstract_excerpt":"An automated and accurate fabric defect inspection system is in high demand as a replacement for slow, inconsistent, error-prone, and expensive human operators in the textile industry. Previous efforts focused on certain types of fabrics or defects, which is not an ideal solution. In this paper, we propose a novel one-class model that is capable of detecting various defects on different fabric types. Our model takes advantage of a well-designed Gabor filter bank to analyze fabric texture. We then leverage an advanced deep learning algorithm, autoencoder, to learn general feature representation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.09648","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2204.09648/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2204.09648","created_at":"2026-07-05T04:16:32.466111+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.09648v1","created_at":"2026-07-05T04:16:32.466111+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.09648","created_at":"2026-07-05T04:16:32.466111+00:00"},{"alias_kind":"pith_short_12","alias_value":"XRPKWD7BVHET","created_at":"2026-07-05T04:16:32.466111+00:00"},{"alias_kind":"pith_short_16","alias_value":"XRPKWD7BVHETQ63H","created_at":"2026-07-05T04:16:32.466111+00:00"},{"alias_kind":"pith_short_8","alias_value":"XRPKWD7B","created_at":"2026-07-05T04:16:32.466111+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/XRPKWD7BVHETQ63HIPE3RN764X","json":"https://pith.science/pith/XRPKWD7BVHETQ63HIPE3RN764X.json","graph_json":"https://pith.science/api/pith-number/XRPKWD7BVHETQ63HIPE3RN764X/graph.json","events_json":"https://pith.science/api/pith-number/XRPKWD7BVHETQ63HIPE3RN764X/events.json","paper":"https://pith.science/paper/XRPKWD7B"},"agent_actions":{"view_html":"https://pith.science/pith/XRPKWD7BVHETQ63HIPE3RN764X","download_json":"https://pith.science/pith/XRPKWD7BVHETQ63HIPE3RN764X.json","view_paper":"https://pith.science/paper/XRPKWD7B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.09648&json=true","fetch_graph":"https://pith.science/api/pith-number/XRPKWD7BVHETQ63HIPE3RN764X/graph.json","fetch_events":"https://pith.science/api/pith-number/XRPKWD7BVHETQ63HIPE3RN764X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XRPKWD7BVHETQ63HIPE3RN764X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XRPKWD7BVHETQ63HIPE3RN764X/action/storage_attestation","attest_author":"https://pith.science/pith/XRPKWD7BVHETQ63HIPE3RN764X/action/author_attestation","sign_citation":"https://pith.science/pith/XRPKWD7BVHETQ63HIPE3RN764X/action/citation_signature","submit_replication":"https://pith.science/pith/XRPKWD7BVHETQ63HIPE3RN764X/action/replication_record"}},"created_at":"2026-07-05T04:16:32.466111+00:00","updated_at":"2026-07-05T04:16:32.466111+00:00"}