{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:X5MDSZ7LOB6AL7E3CROECLKTNA","short_pith_number":"pith:X5MDSZ7L","schema_version":"1.0","canonical_sha256":"bf583967eb707c05fc9b145c412d536803c609431bd348491240c55b3a6251f3","source":{"kind":"arxiv","id":"2501.09579","version":1},"attestation_state":"computed","paper":{"title":"Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Christoph Garth, Juraj Fulir, Petra Gospodneti\\'c, Runzhou Mao","submitted_at":"2025-01-16T14:56:41Z","abstract_excerpt":"The appearance of surface impurities (e.g., water stains, fingerprints, stickers) is an often-mentioned issue that causes degradation of automated visual inspection systems. At the same time, synthetic data generation techniques for visual surface inspection have focused primarily on generating perfect examples and defects, disregarding impurities. This study highlights the importance of considering impurities when generating synthetic data. We introduce a procedural method to include photorealistic water stains in synthetic data. The synthetic datasets are generated to correspond to real data"},"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":"2501.09579","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-16T14:56:41Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"f3986fedd33c6a071c2187ca91dcc3aecb85bd50d2dc1ec21518f8434745668b","abstract_canon_sha256":"b5080a75c203624a7fe8720bdfd6fd50014af675a00819185b2a9bc0be88f371"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:01:49.260049Z","signature_b64":"DrtGTDqoj59rBmJWtWOMFSgqUglTNZFkXniact037x+/0oMiLZ0EF8weutPQXwVUzt6fBlhmOOtGS9dMnOs1Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf583967eb707c05fc9b145c412d536803c609431bd348491240c55b3a6251f3","last_reissued_at":"2026-07-05T10:01:49.259634Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:01:49.259634Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Christoph Garth, Juraj Fulir, Petra Gospodneti\\'c, Runzhou Mao","submitted_at":"2025-01-16T14:56:41Z","abstract_excerpt":"The appearance of surface impurities (e.g., water stains, fingerprints, stickers) is an often-mentioned issue that causes degradation of automated visual inspection systems. At the same time, synthetic data generation techniques for visual surface inspection have focused primarily on generating perfect examples and defects, disregarding impurities. This study highlights the importance of considering impurities when generating synthetic data. We introduce a procedural method to include photorealistic water stains in synthetic data. The synthetic datasets are generated to correspond to real data"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09579","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/2501.09579/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":"2501.09579","created_at":"2026-07-05T10:01:49.259692+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.09579v1","created_at":"2026-07-05T10:01:49.259692+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09579","created_at":"2026-07-05T10:01:49.259692+00:00"},{"alias_kind":"pith_short_12","alias_value":"X5MDSZ7LOB6A","created_at":"2026-07-05T10:01:49.259692+00:00"},{"alias_kind":"pith_short_16","alias_value":"X5MDSZ7LOB6AL7E3","created_at":"2026-07-05T10:01:49.259692+00:00"},{"alias_kind":"pith_short_8","alias_value":"X5MDSZ7L","created_at":"2026-07-05T10:01:49.259692+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/X5MDSZ7LOB6AL7E3CROECLKTNA","json":"https://pith.science/pith/X5MDSZ7LOB6AL7E3CROECLKTNA.json","graph_json":"https://pith.science/api/pith-number/X5MDSZ7LOB6AL7E3CROECLKTNA/graph.json","events_json":"https://pith.science/api/pith-number/X5MDSZ7LOB6AL7E3CROECLKTNA/events.json","paper":"https://pith.science/paper/X5MDSZ7L"},"agent_actions":{"view_html":"https://pith.science/pith/X5MDSZ7LOB6AL7E3CROECLKTNA","download_json":"https://pith.science/pith/X5MDSZ7LOB6AL7E3CROECLKTNA.json","view_paper":"https://pith.science/paper/X5MDSZ7L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.09579&json=true","fetch_graph":"https://pith.science/api/pith-number/X5MDSZ7LOB6AL7E3CROECLKTNA/graph.json","fetch_events":"https://pith.science/api/pith-number/X5MDSZ7LOB6AL7E3CROECLKTNA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X5MDSZ7LOB6AL7E3CROECLKTNA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X5MDSZ7LOB6AL7E3CROECLKTNA/action/storage_attestation","attest_author":"https://pith.science/pith/X5MDSZ7LOB6AL7E3CROECLKTNA/action/author_attestation","sign_citation":"https://pith.science/pith/X5MDSZ7LOB6AL7E3CROECLKTNA/action/citation_signature","submit_replication":"https://pith.science/pith/X5MDSZ7LOB6AL7E3CROECLKTNA/action/replication_record"}},"created_at":"2026-07-05T10:01:49.259692+00:00","updated_at":"2026-07-05T10:01:49.259692+00:00"}