{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:B7JJWNOOFXE4GQF2WIWZJSQ6BP","short_pith_number":"pith:B7JJWNOO","schema_version":"1.0","canonical_sha256":"0fd29b35ce2dc9c340bab22d94ca1e0bf8aa06c7d6384bba64cac2068c1e7d45","source":{"kind":"arxiv","id":"1511.06853","version":1},"attestation_state":"computed","paper":{"title":"TransCut: Transparent Object Segmentation from a Light-Field Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Atsushi Shimada, Hajime Nagahara, Rin-ichiro Taniguchi, Yichao Xu","submitted_at":"2015-11-21T08:33:18Z","abstract_excerpt":"The segmentation of transparent objects can be very useful in computer vision applications. However, because they borrow texture from their background and have a similar appearance to their surroundings, transparent objects are not handled well by regular image segmentation methods. We propose a method that overcomes these problems using the consistency and distortion properties of a light-field image. Graph-cut optimization is applied for the pixel labeling problem. The light-field linearity is used to estimate the likelihood of a pixel belonging to the transparent object or Lambertian backgr"},"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":"1511.06853","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-21T08:33:18Z","cross_cats_sorted":[],"title_canon_sha256":"c9aa60c4bdccc927e0eaf8cc6683afa9ab5952ffc4bd935eee92bdfb04c5fd82","abstract_canon_sha256":"135c46758b31773d9e1f5759c3ba4010bea300a81fc7d4af67e617b69497a717"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:26:17.345249Z","signature_b64":"JHuFWb9jcse+2X96pX90w5sNFWikvwae+vf0QKpkxEA47AwlSwSvqLpFZ/pCnuVxPcPjOzySluB9M81np3nMCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0fd29b35ce2dc9c340bab22d94ca1e0bf8aa06c7d6384bba64cac2068c1e7d45","last_reissued_at":"2026-05-18T01:26:17.344500Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:26:17.344500Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TransCut: Transparent Object Segmentation from a Light-Field Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Atsushi Shimada, Hajime Nagahara, Rin-ichiro Taniguchi, Yichao Xu","submitted_at":"2015-11-21T08:33:18Z","abstract_excerpt":"The segmentation of transparent objects can be very useful in computer vision applications. However, because they borrow texture from their background and have a similar appearance to their surroundings, transparent objects are not handled well by regular image segmentation methods. We propose a method that overcomes these problems using the consistency and distortion properties of a light-field image. Graph-cut optimization is applied for the pixel labeling problem. The light-field linearity is used to estimate the likelihood of a pixel belonging to the transparent object or Lambertian backgr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.06853","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":"1511.06853","created_at":"2026-05-18T01:26:17.344617+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.06853v1","created_at":"2026-05-18T01:26:17.344617+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.06853","created_at":"2026-05-18T01:26:17.344617+00:00"},{"alias_kind":"pith_short_12","alias_value":"B7JJWNOOFXE4","created_at":"2026-05-18T12:29:14.074870+00:00"},{"alias_kind":"pith_short_16","alias_value":"B7JJWNOOFXE4GQF2","created_at":"2026-05-18T12:29:14.074870+00:00"},{"alias_kind":"pith_short_8","alias_value":"B7JJWNOO","created_at":"2026-05-18T12:29:14.074870+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/B7JJWNOOFXE4GQF2WIWZJSQ6BP","json":"https://pith.science/pith/B7JJWNOOFXE4GQF2WIWZJSQ6BP.json","graph_json":"https://pith.science/api/pith-number/B7JJWNOOFXE4GQF2WIWZJSQ6BP/graph.json","events_json":"https://pith.science/api/pith-number/B7JJWNOOFXE4GQF2WIWZJSQ6BP/events.json","paper":"https://pith.science/paper/B7JJWNOO"},"agent_actions":{"view_html":"https://pith.science/pith/B7JJWNOOFXE4GQF2WIWZJSQ6BP","download_json":"https://pith.science/pith/B7JJWNOOFXE4GQF2WIWZJSQ6BP.json","view_paper":"https://pith.science/paper/B7JJWNOO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.06853&json=true","fetch_graph":"https://pith.science/api/pith-number/B7JJWNOOFXE4GQF2WIWZJSQ6BP/graph.json","fetch_events":"https://pith.science/api/pith-number/B7JJWNOOFXE4GQF2WIWZJSQ6BP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B7JJWNOOFXE4GQF2WIWZJSQ6BP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B7JJWNOOFXE4GQF2WIWZJSQ6BP/action/storage_attestation","attest_author":"https://pith.science/pith/B7JJWNOOFXE4GQF2WIWZJSQ6BP/action/author_attestation","sign_citation":"https://pith.science/pith/B7JJWNOOFXE4GQF2WIWZJSQ6BP/action/citation_signature","submit_replication":"https://pith.science/pith/B7JJWNOOFXE4GQF2WIWZJSQ6BP/action/replication_record"}},"created_at":"2026-05-18T01:26:17.344617+00:00","updated_at":"2026-05-18T01:26:17.344617+00:00"}