{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:JZM4VSUWU6MUUTDFGZVVFSQ4WY","short_pith_number":"pith:JZM4VSUW","schema_version":"1.0","canonical_sha256":"4e59caca96a7994a4c65366b52ca1cb623cb5e86192cedf6ce6a6e491cadd13a","source":{"kind":"arxiv","id":"1805.00223","version":2},"attestation_state":"computed","paper":{"title":"Localization: A Missing Link in the Pipeline of Object Matching and Registration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arvinder Singh Soin, Deepak Mishra, Mukul Sarkar, Rajeev Ranjan, Santanu Chaudhury","submitted_at":"2018-05-01T07:50:25Z","abstract_excerpt":"Image registration is a process of aligning two or more images of same objects using geometric transformation. Most of the existing approaches work on the assumption of location invariance. These approaches require object-centric images to perform matching. Further, in absence of intensity level symmetry between the corresponding points in two images, the learning based registration approaches rely on synthetic deformations, which often fail in real scenarios. To address these issues, a combination of convolutional neural networks (CNNs) to perform the desired registration is developed in this"},"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":"1805.00223","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-01T07:50:25Z","cross_cats_sorted":[],"title_canon_sha256":"54166b2932bed4a01369e2900a56804273d52817a433054034f4ac9755c5717b","abstract_canon_sha256":"c802ea9e5246ba87059d9081d410d4ebe7372a1c338e2d8efc98ecb9d2d56699"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:33.994195Z","signature_b64":"bzSXLnAQ+7ucErhAYnsJv62xs5K2j8Q4oQEUhO1QZiG6j803I9LIx2HoW35UcnjPn6tomy9gck+SY+4MPY89DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e59caca96a7994a4c65366b52ca1cb623cb5e86192cedf6ce6a6e491cadd13a","last_reissued_at":"2026-05-17T23:56:33.993528Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:33.993528Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Localization: A Missing Link in the Pipeline of Object Matching and Registration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arvinder Singh Soin, Deepak Mishra, Mukul Sarkar, Rajeev Ranjan, Santanu Chaudhury","submitted_at":"2018-05-01T07:50:25Z","abstract_excerpt":"Image registration is a process of aligning two or more images of same objects using geometric transformation. Most of the existing approaches work on the assumption of location invariance. These approaches require object-centric images to perform matching. Further, in absence of intensity level symmetry between the corresponding points in two images, the learning based registration approaches rely on synthetic deformations, which often fail in real scenarios. To address these issues, a combination of convolutional neural networks (CNNs) to perform the desired registration is developed in this"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.00223","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":""},"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":"1805.00223","created_at":"2026-05-17T23:56:33.993613+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.00223v2","created_at":"2026-05-17T23:56:33.993613+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.00223","created_at":"2026-05-17T23:56:33.993613+00:00"},{"alias_kind":"pith_short_12","alias_value":"JZM4VSUWU6MU","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"JZM4VSUWU6MUUTDF","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"JZM4VSUW","created_at":"2026-05-18T12:32:33.847187+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/JZM4VSUWU6MUUTDFGZVVFSQ4WY","json":"https://pith.science/pith/JZM4VSUWU6MUUTDFGZVVFSQ4WY.json","graph_json":"https://pith.science/api/pith-number/JZM4VSUWU6MUUTDFGZVVFSQ4WY/graph.json","events_json":"https://pith.science/api/pith-number/JZM4VSUWU6MUUTDFGZVVFSQ4WY/events.json","paper":"https://pith.science/paper/JZM4VSUW"},"agent_actions":{"view_html":"https://pith.science/pith/JZM4VSUWU6MUUTDFGZVVFSQ4WY","download_json":"https://pith.science/pith/JZM4VSUWU6MUUTDFGZVVFSQ4WY.json","view_paper":"https://pith.science/paper/JZM4VSUW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.00223&json=true","fetch_graph":"https://pith.science/api/pith-number/JZM4VSUWU6MUUTDFGZVVFSQ4WY/graph.json","fetch_events":"https://pith.science/api/pith-number/JZM4VSUWU6MUUTDFGZVVFSQ4WY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JZM4VSUWU6MUUTDFGZVVFSQ4WY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JZM4VSUWU6MUUTDFGZVVFSQ4WY/action/storage_attestation","attest_author":"https://pith.science/pith/JZM4VSUWU6MUUTDFGZVVFSQ4WY/action/author_attestation","sign_citation":"https://pith.science/pith/JZM4VSUWU6MUUTDFGZVVFSQ4WY/action/citation_signature","submit_replication":"https://pith.science/pith/JZM4VSUWU6MUUTDFGZVVFSQ4WY/action/replication_record"}},"created_at":"2026-05-17T23:56:33.993613+00:00","updated_at":"2026-05-17T23:56:33.993613+00:00"}