{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:CR57GSVGKABU2BI6OB5LAEQUF7","short_pith_number":"pith:CR57GSVG","schema_version":"1.0","canonical_sha256":"147bf34aa650034d051e707ab012142fca5ae56486f8788d6d1b319eee5af758","source":{"kind":"arxiv","id":"1807.03442","version":1},"attestation_state":"computed","paper":{"title":"Signals as Parametric Curves: Application to Independent Component Analysis and Blind Source Separation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Anand Rangarajan, Birmingham Hang Guan","submitted_at":"2018-07-10T01:39:35Z","abstract_excerpt":"Images Stacks as Parametric Surfaces (ISPS) is a powerful model that was originally proposed for image registration. Being closely related to mutual information (MI) - the most classic similarity measure for image registration, ISPS works well across different categories of registration problems. The Signals as Parametric Curves (SPC) model is derived from ISPS extended to 1-dimensional signals. Blind Source Separation (BSS) is a classic problem in signal processing, where Independent Component Analysis (ICA) based approaches are popular and effective. Since MI plays an important role in ICA, "},"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":"1807.03442","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-07-10T01:39:35Z","cross_cats_sorted":[],"title_canon_sha256":"a1013b1368301d53346bd4ade152e600dea736c2eb03eee58224a14fc3db08d9","abstract_canon_sha256":"30f43870c8a061bfb6e72f8bb152276a7700e719234f8896361d1f6928c6e8e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:10.431908Z","signature_b64":"Xnjtvt62OzyRsOVGfYWTHGmbzN0DkqCQO4RrSat/Mv9EitjsUv/MmKMX2zr81oOUi6JJDo0/0mVXK5DXFQcBAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"147bf34aa650034d051e707ab012142fca5ae56486f8788d6d1b319eee5af758","last_reissued_at":"2026-05-18T00:11:10.431234Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:10.431234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Signals as Parametric Curves: Application to Independent Component Analysis and Blind Source Separation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Anand Rangarajan, Birmingham Hang Guan","submitted_at":"2018-07-10T01:39:35Z","abstract_excerpt":"Images Stacks as Parametric Surfaces (ISPS) is a powerful model that was originally proposed for image registration. Being closely related to mutual information (MI) - the most classic similarity measure for image registration, ISPS works well across different categories of registration problems. The Signals as Parametric Curves (SPC) model is derived from ISPS extended to 1-dimensional signals. Blind Source Separation (BSS) is a classic problem in signal processing, where Independent Component Analysis (ICA) based approaches are popular and effective. Since MI plays an important role in ICA, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03442","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1807.03442","created_at":"2026-05-18T00:11:10.431329+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.03442v1","created_at":"2026-05-18T00:11:10.431329+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03442","created_at":"2026-05-18T00:11:10.431329+00:00"},{"alias_kind":"pith_short_12","alias_value":"CR57GSVGKABU","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"CR57GSVGKABU2BI6","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"CR57GSVG","created_at":"2026-05-18T12:32:16.446611+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/CR57GSVGKABU2BI6OB5LAEQUF7","json":"https://pith.science/pith/CR57GSVGKABU2BI6OB5LAEQUF7.json","graph_json":"https://pith.science/api/pith-number/CR57GSVGKABU2BI6OB5LAEQUF7/graph.json","events_json":"https://pith.science/api/pith-number/CR57GSVGKABU2BI6OB5LAEQUF7/events.json","paper":"https://pith.science/paper/CR57GSVG"},"agent_actions":{"view_html":"https://pith.science/pith/CR57GSVGKABU2BI6OB5LAEQUF7","download_json":"https://pith.science/pith/CR57GSVGKABU2BI6OB5LAEQUF7.json","view_paper":"https://pith.science/paper/CR57GSVG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.03442&json=true","fetch_graph":"https://pith.science/api/pith-number/CR57GSVGKABU2BI6OB5LAEQUF7/graph.json","fetch_events":"https://pith.science/api/pith-number/CR57GSVGKABU2BI6OB5LAEQUF7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CR57GSVGKABU2BI6OB5LAEQUF7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CR57GSVGKABU2BI6OB5LAEQUF7/action/storage_attestation","attest_author":"https://pith.science/pith/CR57GSVGKABU2BI6OB5LAEQUF7/action/author_attestation","sign_citation":"https://pith.science/pith/CR57GSVGKABU2BI6OB5LAEQUF7/action/citation_signature","submit_replication":"https://pith.science/pith/CR57GSVGKABU2BI6OB5LAEQUF7/action/replication_record"}},"created_at":"2026-05-18T00:11:10.431329+00:00","updated_at":"2026-05-18T00:11:10.431329+00:00"}