{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:GY6PRTP3DXL7A53UUSDW2M6W4P","short_pith_number":"pith:GY6PRTP3","canonical_record":{"source":{"id":"1501.05617","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-01-22T20:04:20Z","cross_cats_sorted":[],"title_canon_sha256":"314312c28b801d6d13137b5e1e717919ea8245e80127554b82f18aab08b9e5e1","abstract_canon_sha256":"1a59eb72d6619e2d372aa0c12b2f9931a9806fa5cd6031b4b0d1d91724f9a619"},"schema_version":"1.0"},"canonical_sha256":"363cf8cdfb1dd7f07774a4876d33d6e3dfb026468d59c37f899d5d3bb0e5b816","source":{"kind":"arxiv","id":"1501.05617","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1501.05617","created_at":"2026-05-18T02:28:54Z"},{"alias_kind":"arxiv_version","alias_value":"1501.05617v1","created_at":"2026-05-18T02:28:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.05617","created_at":"2026-05-18T02:28:54Z"},{"alias_kind":"pith_short_12","alias_value":"GY6PRTP3DXL7","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_16","alias_value":"GY6PRTP3DXL7A53U","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_8","alias_value":"GY6PRTP3","created_at":"2026-05-18T12:29:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:GY6PRTP3DXL7A53UUSDW2M6W4P","target":"record","payload":{"canonical_record":{"source":{"id":"1501.05617","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-01-22T20:04:20Z","cross_cats_sorted":[],"title_canon_sha256":"314312c28b801d6d13137b5e1e717919ea8245e80127554b82f18aab08b9e5e1","abstract_canon_sha256":"1a59eb72d6619e2d372aa0c12b2f9931a9806fa5cd6031b4b0d1d91724f9a619"},"schema_version":"1.0"},"canonical_sha256":"363cf8cdfb1dd7f07774a4876d33d6e3dfb026468d59c37f899d5d3bb0e5b816","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:28:54.357432Z","signature_b64":"/Wk2S1YljLgpbrODUXwcMaWAA72FhhGGH+2jiD17BP4J6dvsMpSgEV1O1b8fW1c3ZWlEUj1wYx4am8OsIUuxDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"363cf8cdfb1dd7f07774a4876d33d6e3dfb026468d59c37f899d5d3bb0e5b816","last_reissued_at":"2026-05-18T02:28:54.357041Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:28:54.357041Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1501.05617","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":"2026-05-18T02:28:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lI5Ho7hF0kEYMUk3N8+GttdxrW2pbhzgMRMqMNZY0Cq0GHZYW92uRlY5n4LEt29ZZsqXl77eMmFkNbxR0kT7AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T19:51:15.178350Z"},"content_sha256":"f07719b12904cfb0ef3997309801244ccbebc3b41959cfe4bea1c571b090f825","schema_version":"1.0","event_id":"sha256:f07719b12904cfb0ef3997309801244ccbebc3b41959cfe4bea1c571b090f825"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:GY6PRTP3DXL7A53UUSDW2M6W4P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised image segmentation by Global and local Criteria Optimization Based on Bayesian Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mohamed Ali Mahjoub, Mohamed Mhiri","submitted_at":"2015-01-22T20:04:20Z","abstract_excerpt":"Today Bayesian networks are more used in many areas of decision support and image processing. In this way, our proposed approach uses Bayesian Network to modelize the segmented image quality. This quality is calculated on a set of attributes that represent local evaluation measures. The idea is to have these local levels chosen in a way to be intersected into them to keep the overall appearance of segmentation. The approach operates in two phases: the first phase is to make an over-segmentation which gives superpixels card. In the second phase, we model the superpixels by a Bayesian Network. T"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.05617","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":"2026-05-18T02:28:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fbl98Wcyo7NKROZfn6lM4Czb4UlUz7Ekrx4uzJ2I5zx0wO97BHO2e0SmudOZ9jJBVX+pmh/d8qa2dBOfWvTaAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T19:51:15.178695Z"},"content_sha256":"3e760d7ebb6cc159b9a2b3ca288874100969c8a22be30be523069288a8ae593a","schema_version":"1.0","event_id":"sha256:3e760d7ebb6cc159b9a2b3ca288874100969c8a22be30be523069288a8ae593a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GY6PRTP3DXL7A53UUSDW2M6W4P/bundle.json","state_url":"https://pith.science/pith/GY6PRTP3DXL7A53UUSDW2M6W4P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GY6PRTP3DXL7A53UUSDW2M6W4P/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":"2026-07-03T19:51:15Z","links":{"resolver":"https://pith.science/pith/GY6PRTP3DXL7A53UUSDW2M6W4P","bundle":"https://pith.science/pith/GY6PRTP3DXL7A53UUSDW2M6W4P/bundle.json","state":"https://pith.science/pith/GY6PRTP3DXL7A53UUSDW2M6W4P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GY6PRTP3DXL7A53UUSDW2M6W4P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:GY6PRTP3DXL7A53UUSDW2M6W4P","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":"1a59eb72d6619e2d372aa0c12b2f9931a9806fa5cd6031b4b0d1d91724f9a619","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-01-22T20:04:20Z","title_canon_sha256":"314312c28b801d6d13137b5e1e717919ea8245e80127554b82f18aab08b9e5e1"},"schema_version":"1.0","source":{"id":"1501.05617","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1501.05617","created_at":"2026-05-18T02:28:54Z"},{"alias_kind":"arxiv_version","alias_value":"1501.05617v1","created_at":"2026-05-18T02:28:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.05617","created_at":"2026-05-18T02:28:54Z"},{"alias_kind":"pith_short_12","alias_value":"GY6PRTP3DXL7","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_16","alias_value":"GY6PRTP3DXL7A53U","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_8","alias_value":"GY6PRTP3","created_at":"2026-05-18T12:29:22Z"}],"graph_snapshots":[{"event_id":"sha256:3e760d7ebb6cc159b9a2b3ca288874100969c8a22be30be523069288a8ae593a","target":"graph","created_at":"2026-05-18T02:28:54Z","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":"Today Bayesian networks are more used in many areas of decision support and image processing. In this way, our proposed approach uses Bayesian Network to modelize the segmented image quality. This quality is calculated on a set of attributes that represent local evaluation measures. The idea is to have these local levels chosen in a way to be intersected into them to keep the overall appearance of segmentation. The approach operates in two phases: the first phase is to make an over-segmentation which gives superpixels card. In the second phase, we model the superpixels by a Bayesian Network. T","authors_text":"Mohamed Ali Mahjoub, Mohamed Mhiri","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-01-22T20:04:20Z","title":"Unsupervised image segmentation by Global and local Criteria Optimization Based on Bayesian Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.05617","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:f07719b12904cfb0ef3997309801244ccbebc3b41959cfe4bea1c571b090f825","target":"record","created_at":"2026-05-18T02:28:54Z","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":"1a59eb72d6619e2d372aa0c12b2f9931a9806fa5cd6031b4b0d1d91724f9a619","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-01-22T20:04:20Z","title_canon_sha256":"314312c28b801d6d13137b5e1e717919ea8245e80127554b82f18aab08b9e5e1"},"schema_version":"1.0","source":{"id":"1501.05617","kind":"arxiv","version":1}},"canonical_sha256":"363cf8cdfb1dd7f07774a4876d33d6e3dfb026468d59c37f899d5d3bb0e5b816","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"363cf8cdfb1dd7f07774a4876d33d6e3dfb026468d59c37f899d5d3bb0e5b816","first_computed_at":"2026-05-18T02:28:54.357041Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:28:54.357041Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/Wk2S1YljLgpbrODUXwcMaWAA72FhhGGH+2jiD17BP4J6dvsMpSgEV1O1b8fW1c3ZWlEUj1wYx4am8OsIUuxDw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:28:54.357432Z","signed_message":"canonical_sha256_bytes"},"source_id":"1501.05617","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f07719b12904cfb0ef3997309801244ccbebc3b41959cfe4bea1c571b090f825","sha256:3e760d7ebb6cc159b9a2b3ca288874100969c8a22be30be523069288a8ae593a"],"state_sha256":"1f198dcf72dd1a826fd78a66cd8a2ecab5d9306bddc3a77d47472dc1e30efcf2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aSqTcl8AmPGE36T+jl5MmomHRD1xRbaSCKnrkwHncRx/17uUhbFaWJT8wfb6u3aao4NQDz9S1M2JsEOI2zPIDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T19:51:15.180574Z","bundle_sha256":"7dbd2c70c1541e720a641a12ed4cfff26790cb678b7926876ea332b2c8a2d52a"}}