{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:VBJKNT7IDEXJ56IEFUDADX4VJC","short_pith_number":"pith:VBJKNT7I","canonical_record":{"source":{"id":"1802.09816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-27T10:47:06Z","cross_cats_sorted":["cs.AI","cs.LG","cs.NE","stat.ML"],"title_canon_sha256":"3ae1c6c5486e50ec6d50b7e20e3723a32ffae2f81b4dfe0d95a7fab2a83633db","abstract_canon_sha256":"d1797af80b2281a7361ea9578cffa47632b8d5a7fd34583d22232873f18cf639"},"schema_version":"1.0"},"canonical_sha256":"a852a6cfe8192e9ef9042d0601df9548886e0be304620c18746f9a953df0b24b","source":{"kind":"arxiv","id":"1802.09816","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.09816","created_at":"2026-05-18T00:22:22Z"},{"alias_kind":"arxiv_version","alias_value":"1802.09816v1","created_at":"2026-05-18T00:22:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.09816","created_at":"2026-05-18T00:22:22Z"},{"alias_kind":"pith_short_12","alias_value":"VBJKNT7IDEXJ","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VBJKNT7IDEXJ56IE","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VBJKNT7I","created_at":"2026-05-18T12:32:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:VBJKNT7IDEXJ56IEFUDADX4VJC","target":"record","payload":{"canonical_record":{"source":{"id":"1802.09816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-27T10:47:06Z","cross_cats_sorted":["cs.AI","cs.LG","cs.NE","stat.ML"],"title_canon_sha256":"3ae1c6c5486e50ec6d50b7e20e3723a32ffae2f81b4dfe0d95a7fab2a83633db","abstract_canon_sha256":"d1797af80b2281a7361ea9578cffa47632b8d5a7fd34583d22232873f18cf639"},"schema_version":"1.0"},"canonical_sha256":"a852a6cfe8192e9ef9042d0601df9548886e0be304620c18746f9a953df0b24b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:22.107346Z","signature_b64":"krvS925e1hXmn7qS1m235DmbBbb6sriJUIgRLTCmQJWl+jAP6bmfqKEtwK4GApXjHmu4HJEp8KyVrImzu+djBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a852a6cfe8192e9ef9042d0601df9548886e0be304620c18746f9a953df0b24b","last_reissued_at":"2026-05-18T00:22:22.106772Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:22.106772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.09816","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:22:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eijTkSOF5DmzWqeok5nC3rbAHW/UB0ImCdsbUAjptICO8b+hcjTrvq+BJlVyCSOUK8DwtF6Wb/OnkHG+rp5hDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T00:46:26.807938Z"},"content_sha256":"75e4d1ce0f8fbea59fc20829f7a278b72ead3b824ec7843d734135fe905df993","schema_version":"1.0","event_id":"sha256:75e4d1ce0f8fbea59fc20829f7a278b72ead3b824ec7843d734135fe905df993"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:VBJKNT7IDEXJ56IEFUDADX4VJC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.NE","stat.ML"],"primary_cat":"cs.CV","authors_text":"Armand Zampieri (TITANE), Guillaume Charpiat (TAU), Yuliya Tarabalka (TITANE)","submitted_at":"2018-02-27T10:47:06Z","abstract_excerpt":"We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neural networks able to learn scale-specific features. Once chained appropriately, they perform global registration in linear time, getting rid of gradient descent schemes by predicting directly the deformat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.09816","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:22:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"49hgROmmgx85kcXwIXf2aU2taYfGblhNbZW3ZNxNhytaZfAaL0x62JL7K5YA0JYonAGeuVRBXJyLoa1s5BZ4Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T00:46:26.808588Z"},"content_sha256":"37735ed74e2dc21e417b9955eb341aa5fb39e0dde4f0537a4179469a2eead2b1","schema_version":"1.0","event_id":"sha256:37735ed74e2dc21e417b9955eb341aa5fb39e0dde4f0537a4179469a2eead2b1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VBJKNT7IDEXJ56IEFUDADX4VJC/bundle.json","state_url":"https://pith.science/pith/VBJKNT7IDEXJ56IEFUDADX4VJC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VBJKNT7IDEXJ56IEFUDADX4VJC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-04T00:46:26Z","links":{"resolver":"https://pith.science/pith/VBJKNT7IDEXJ56IEFUDADX4VJC","bundle":"https://pith.science/pith/VBJKNT7IDEXJ56IEFUDADX4VJC/bundle.json","state":"https://pith.science/pith/VBJKNT7IDEXJ56IEFUDADX4VJC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VBJKNT7IDEXJ56IEFUDADX4VJC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:VBJKNT7IDEXJ56IEFUDADX4VJC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d1797af80b2281a7361ea9578cffa47632b8d5a7fd34583d22232873f18cf639","cross_cats_sorted":["cs.AI","cs.LG","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-27T10:47:06Z","title_canon_sha256":"3ae1c6c5486e50ec6d50b7e20e3723a32ffae2f81b4dfe0d95a7fab2a83633db"},"schema_version":"1.0","source":{"id":"1802.09816","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.09816","created_at":"2026-05-18T00:22:22Z"},{"alias_kind":"arxiv_version","alias_value":"1802.09816v1","created_at":"2026-05-18T00:22:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.09816","created_at":"2026-05-18T00:22:22Z"},{"alias_kind":"pith_short_12","alias_value":"VBJKNT7IDEXJ","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VBJKNT7IDEXJ56IE","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VBJKNT7I","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:37735ed74e2dc21e417b9955eb341aa5fb39e0dde4f0537a4179469a2eead2b1","target":"graph","created_at":"2026-05-18T00:22:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neural networks able to learn scale-specific features. Once chained appropriately, they perform global registration in linear time, getting rid of gradient descent schemes by predicting directly the deformat","authors_text":"Armand Zampieri (TITANE), Guillaume Charpiat (TAU), Yuliya Tarabalka (TITANE)","cross_cats":["cs.AI","cs.LG","cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-27T10:47:06Z","title":"Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.09816","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:75e4d1ce0f8fbea59fc20829f7a278b72ead3b824ec7843d734135fe905df993","target":"record","created_at":"2026-05-18T00:22:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d1797af80b2281a7361ea9578cffa47632b8d5a7fd34583d22232873f18cf639","cross_cats_sorted":["cs.AI","cs.LG","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-27T10:47:06Z","title_canon_sha256":"3ae1c6c5486e50ec6d50b7e20e3723a32ffae2f81b4dfe0d95a7fab2a83633db"},"schema_version":"1.0","source":{"id":"1802.09816","kind":"arxiv","version":1}},"canonical_sha256":"a852a6cfe8192e9ef9042d0601df9548886e0be304620c18746f9a953df0b24b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a852a6cfe8192e9ef9042d0601df9548886e0be304620c18746f9a953df0b24b","first_computed_at":"2026-05-18T00:22:22.106772Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:22:22.106772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"krvS925e1hXmn7qS1m235DmbBbb6sriJUIgRLTCmQJWl+jAP6bmfqKEtwK4GApXjHmu4HJEp8KyVrImzu+djBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:22:22.107346Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.09816","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75e4d1ce0f8fbea59fc20829f7a278b72ead3b824ec7843d734135fe905df993","sha256:37735ed74e2dc21e417b9955eb341aa5fb39e0dde4f0537a4179469a2eead2b1"],"state_sha256":"6a57d9f5595e29df02598862c320845bf72b9f379272ba918e7f1a0b463d9292"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k26lPAb9sgck1h3NtO6D+45v5bVqk56TyE7IKd9jen+VTrJlsm7CBEilXw87U03nGs6Dids4jHtKmwlnJtaVDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T00:46:26.812195Z","bundle_sha256":"501933d46771ac9b215ad8a4838af40d9a9a6015b10df92f80f399257feb2fec"}}