{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZUVNV3C33DCCEDMS5U3RXLOASH","short_pith_number":"pith:ZUVNV3C3","schema_version":"1.0","canonical_sha256":"cd2adaec5bd8c4220d92ed371badc091c6582de0ea026bdbf3841500c5658c90","source":{"kind":"arxiv","id":"1805.09408","version":1},"attestation_state":"computed","paper":{"title":"Non-convex non-local flows for saliency detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.NA"],"primary_cat":"cs.CV","authors_text":"Emanuele Schiavi, Gonzalo Galiano, Iv\\'an Ram\\'irez","submitted_at":"2018-05-23T20:03:06Z","abstract_excerpt":"We propose and numerically solve a new variational model for automatic saliency detection in digital images. Using a non-local framework we consider a family of edge preserving functions combined with a new quadratic saliency detection term. Such term defines a constrained bilateral obstacle problem for image classification driven by p-Laplacian operators, including the so-called hyper-Laplacian case (0 < p < 1). The related non-convex non-local reactive flows are then considered and applied for glioblastoma segmentation in magnetic resonance fluid-attenuated inversion recovery (MRI-Flair) ima"},"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":"1805.09408","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-05-23T20:03:06Z","cross_cats_sorted":["math.NA"],"title_canon_sha256":"e304c83721fc552c5faefb9c70d5273d43476594fccf58d469098049420af8cc","abstract_canon_sha256":"8c36e796cd4c6169950477bfd72d97339213730e283d3cd2a32f7b242ebcbbe3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:04.060090Z","signature_b64":"LvNSEhy0KBRdQO2TEG+6c8tvzVORN70F3zChAKnZamxdskGrjVK+6jt1gRDZEG6P0W6kAlbrRdxy8e7lDMJqAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd2adaec5bd8c4220d92ed371badc091c6582de0ea026bdbf3841500c5658c90","last_reissued_at":"2026-05-18T00:15:04.059498Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:04.059498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Non-convex non-local flows for saliency detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.NA"],"primary_cat":"cs.CV","authors_text":"Emanuele Schiavi, Gonzalo Galiano, Iv\\'an Ram\\'irez","submitted_at":"2018-05-23T20:03:06Z","abstract_excerpt":"We propose and numerically solve a new variational model for automatic saliency detection in digital images. Using a non-local framework we consider a family of edge preserving functions combined with a new quadratic saliency detection term. Such term defines a constrained bilateral obstacle problem for image classification driven by p-Laplacian operators, including the so-called hyper-Laplacian case (0 < p < 1). The related non-convex non-local reactive flows are then considered and applied for glioblastoma segmentation in magnetic resonance fluid-attenuated inversion recovery (MRI-Flair) ima"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.09408","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":"1805.09408","created_at":"2026-05-18T00:15:04.059591+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.09408v1","created_at":"2026-05-18T00:15:04.059591+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.09408","created_at":"2026-05-18T00:15:04.059591+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZUVNV3C33DCC","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZUVNV3C33DCCEDMS","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZUVNV3C3","created_at":"2026-05-18T12:33:07.085635+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/ZUVNV3C33DCCEDMS5U3RXLOASH","json":"https://pith.science/pith/ZUVNV3C33DCCEDMS5U3RXLOASH.json","graph_json":"https://pith.science/api/pith-number/ZUVNV3C33DCCEDMS5U3RXLOASH/graph.json","events_json":"https://pith.science/api/pith-number/ZUVNV3C33DCCEDMS5U3RXLOASH/events.json","paper":"https://pith.science/paper/ZUVNV3C3"},"agent_actions":{"view_html":"https://pith.science/pith/ZUVNV3C33DCCEDMS5U3RXLOASH","download_json":"https://pith.science/pith/ZUVNV3C33DCCEDMS5U3RXLOASH.json","view_paper":"https://pith.science/paper/ZUVNV3C3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.09408&json=true","fetch_graph":"https://pith.science/api/pith-number/ZUVNV3C33DCCEDMS5U3RXLOASH/graph.json","fetch_events":"https://pith.science/api/pith-number/ZUVNV3C33DCCEDMS5U3RXLOASH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZUVNV3C33DCCEDMS5U3RXLOASH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZUVNV3C33DCCEDMS5U3RXLOASH/action/storage_attestation","attest_author":"https://pith.science/pith/ZUVNV3C33DCCEDMS5U3RXLOASH/action/author_attestation","sign_citation":"https://pith.science/pith/ZUVNV3C33DCCEDMS5U3RXLOASH/action/citation_signature","submit_replication":"https://pith.science/pith/ZUVNV3C33DCCEDMS5U3RXLOASH/action/replication_record"}},"created_at":"2026-05-18T00:15:04.059591+00:00","updated_at":"2026-05-18T00:15:04.059591+00:00"}