{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:D75PF7KFX3VYLFGT2QH533D7GX","short_pith_number":"pith:D75PF7KF","schema_version":"1.0","canonical_sha256":"1ffaf2fd45beeb8594d3d40fddec7f35d7b7276fb525a326f0a59ec2aecde58c","source":{"kind":"arxiv","id":"1603.09599","version":1},"attestation_state":"computed","paper":{"title":"Total Variation Applications in Computer Vision","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hermes Aguiar Magalhaes, Osamu Saotome, Vania V. Estrela","submitted_at":"2016-03-31T14:08:53Z","abstract_excerpt":"The objectives of this chapter are: (i) to introduce a concise overview of regularization; (ii) to define and to explain the role of a particular type of regularization called total variation norm (TV-norm) in computer vision tasks; (iii) to set up a brief discussion on the mathematical background of TV methods; and (iv) to establish a relationship between models and a few existing methods to solve problems cast as TV-norm. For the most part, image-processing algorithms blur the edges of the estimated images, however TV regularization preserves the edges with no prior information on the observ"},"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":"1603.09599","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2016-03-31T14:08:53Z","cross_cats_sorted":[],"title_canon_sha256":"6752a1b0b4ecc34dfad06337d395e8f2764b840af168cb72d615a8571f109347","abstract_canon_sha256":"5e95af4c83567a5dd59e4c27ed1f747d4384c321eaf7f331acc0bed4f604c63d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:58.071957Z","signature_b64":"V6MQZhxN9ijMPFj95AIBetNuf1r5UY++OU/8MtFxgyuA5yyNtn7CQvFXtv/ohAaELozELzY1pnB0E878dKYBBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ffaf2fd45beeb8594d3d40fddec7f35d7b7276fb525a326f0a59ec2aecde58c","last_reissued_at":"2026-05-18T01:17:58.071266Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:58.071266Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Total Variation Applications in Computer Vision","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hermes Aguiar Magalhaes, Osamu Saotome, Vania V. Estrela","submitted_at":"2016-03-31T14:08:53Z","abstract_excerpt":"The objectives of this chapter are: (i) to introduce a concise overview of regularization; (ii) to define and to explain the role of a particular type of regularization called total variation norm (TV-norm) in computer vision tasks; (iii) to set up a brief discussion on the mathematical background of TV methods; and (iv) to establish a relationship between models and a few existing methods to solve problems cast as TV-norm. For the most part, image-processing algorithms blur the edges of the estimated images, however TV regularization preserves the edges with no prior information on the observ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.09599","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":"1603.09599","created_at":"2026-05-18T01:17:58.071367+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.09599v1","created_at":"2026-05-18T01:17:58.071367+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.09599","created_at":"2026-05-18T01:17:58.071367+00:00"},{"alias_kind":"pith_short_12","alias_value":"D75PF7KFX3VY","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_16","alias_value":"D75PF7KFX3VYLFGT","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_8","alias_value":"D75PF7KF","created_at":"2026-05-18T12:30:09.641336+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2510.06194","citing_title":"Overlap-aware segmentation for topological reconstruction of obscured objects","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D75PF7KFX3VYLFGT2QH533D7GX","json":"https://pith.science/pith/D75PF7KFX3VYLFGT2QH533D7GX.json","graph_json":"https://pith.science/api/pith-number/D75PF7KFX3VYLFGT2QH533D7GX/graph.json","events_json":"https://pith.science/api/pith-number/D75PF7KFX3VYLFGT2QH533D7GX/events.json","paper":"https://pith.science/paper/D75PF7KF"},"agent_actions":{"view_html":"https://pith.science/pith/D75PF7KFX3VYLFGT2QH533D7GX","download_json":"https://pith.science/pith/D75PF7KFX3VYLFGT2QH533D7GX.json","view_paper":"https://pith.science/paper/D75PF7KF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.09599&json=true","fetch_graph":"https://pith.science/api/pith-number/D75PF7KFX3VYLFGT2QH533D7GX/graph.json","fetch_events":"https://pith.science/api/pith-number/D75PF7KFX3VYLFGT2QH533D7GX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D75PF7KFX3VYLFGT2QH533D7GX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D75PF7KFX3VYLFGT2QH533D7GX/action/storage_attestation","attest_author":"https://pith.science/pith/D75PF7KFX3VYLFGT2QH533D7GX/action/author_attestation","sign_citation":"https://pith.science/pith/D75PF7KFX3VYLFGT2QH533D7GX/action/citation_signature","submit_replication":"https://pith.science/pith/D75PF7KFX3VYLFGT2QH533D7GX/action/replication_record"}},"created_at":"2026-05-18T01:17:58.071367+00:00","updated_at":"2026-05-18T01:17:58.071367+00:00"}