{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:GGEIXQVW7OLF5QCV55B3PTV44J","short_pith_number":"pith:GGEIXQVW","schema_version":"1.0","canonical_sha256":"31888bc2b6fb965ec055ef43b7cebce263d71b8fd22cd6618ad12d3fff06e9f3","source":{"kind":"arxiv","id":"2208.04884","version":2},"attestation_state":"computed","paper":{"title":"Localizing the conceptual difference of two scenes using deep learning for house keeping usages","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ali Atghaei, Ehsan Rahnama, Kiavash Azimi","submitted_at":"2022-08-09T16:25:56Z","abstract_excerpt":"Finding the conceptual difference between the two images in an industrial environment has been especially important for HSE purposes and there is still no reliable and conformable method to find the major differences to alert the related controllers. Due to the abundance and variety of objects in different environments, the use of supervised learning methods in this field is facing a major problem. Due to the sharp and even slight change in lighting conditions in the two scenes, it is not possible to naively subtract the two images in order to find these differences. The goal of this paper is "},"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":"2208.04884","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-08-09T16:25:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"61342f46972b1d82ddad8c73428f1b7422c25cbdd6271df8d11ec4868820e1e1","abstract_canon_sha256":"b36134b02d4ebb27b893b1e86fc03938d3b0a3507ec164886a163a375cbf2bff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:23:46.175289Z","signature_b64":"j76oD8E29QWnpWBCNRSxwJS5t8UzXWJI9pax6XgajmBRIUnQxecgCB6GNDPzTwiV7SQFIWVnUTu8+tRizaRQDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31888bc2b6fb965ec055ef43b7cebce263d71b8fd22cd6618ad12d3fff06e9f3","last_reissued_at":"2026-07-05T05:23:46.174797Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:23:46.174797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Localizing the conceptual difference of two scenes using deep learning for house keeping usages","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ali Atghaei, Ehsan Rahnama, Kiavash Azimi","submitted_at":"2022-08-09T16:25:56Z","abstract_excerpt":"Finding the conceptual difference between the two images in an industrial environment has been especially important for HSE purposes and there is still no reliable and conformable method to find the major differences to alert the related controllers. Due to the abundance and variety of objects in different environments, the use of supervised learning methods in this field is facing a major problem. Due to the sharp and even slight change in lighting conditions in the two scenes, it is not possible to naively subtract the two images in order to find these differences. The goal of this paper is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.04884","kind":"arxiv","version":2},"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/2208.04884/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":"2208.04884","created_at":"2026-07-05T05:23:46.174858+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.04884v2","created_at":"2026-07-05T05:23:46.174858+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.04884","created_at":"2026-07-05T05:23:46.174858+00:00"},{"alias_kind":"pith_short_12","alias_value":"GGEIXQVW7OLF","created_at":"2026-07-05T05:23:46.174858+00:00"},{"alias_kind":"pith_short_16","alias_value":"GGEIXQVW7OLF5QCV","created_at":"2026-07-05T05:23:46.174858+00:00"},{"alias_kind":"pith_short_8","alias_value":"GGEIXQVW","created_at":"2026-07-05T05:23:46.174858+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/GGEIXQVW7OLF5QCV55B3PTV44J","json":"https://pith.science/pith/GGEIXQVW7OLF5QCV55B3PTV44J.json","graph_json":"https://pith.science/api/pith-number/GGEIXQVW7OLF5QCV55B3PTV44J/graph.json","events_json":"https://pith.science/api/pith-number/GGEIXQVW7OLF5QCV55B3PTV44J/events.json","paper":"https://pith.science/paper/GGEIXQVW"},"agent_actions":{"view_html":"https://pith.science/pith/GGEIXQVW7OLF5QCV55B3PTV44J","download_json":"https://pith.science/pith/GGEIXQVW7OLF5QCV55B3PTV44J.json","view_paper":"https://pith.science/paper/GGEIXQVW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.04884&json=true","fetch_graph":"https://pith.science/api/pith-number/GGEIXQVW7OLF5QCV55B3PTV44J/graph.json","fetch_events":"https://pith.science/api/pith-number/GGEIXQVW7OLF5QCV55B3PTV44J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GGEIXQVW7OLF5QCV55B3PTV44J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GGEIXQVW7OLF5QCV55B3PTV44J/action/storage_attestation","attest_author":"https://pith.science/pith/GGEIXQVW7OLF5QCV55B3PTV44J/action/author_attestation","sign_citation":"https://pith.science/pith/GGEIXQVW7OLF5QCV55B3PTV44J/action/citation_signature","submit_replication":"https://pith.science/pith/GGEIXQVW7OLF5QCV55B3PTV44J/action/replication_record"}},"created_at":"2026-07-05T05:23:46.174858+00:00","updated_at":"2026-07-05T05:23:46.174858+00:00"}