{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:LQSBG4L2B3TJQAGXTRO4SXRL3D","short_pith_number":"pith:LQSBG4L2","canonical_record":{"source":{"id":"1811.01526","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-11-05T06:11:24Z","cross_cats_sorted":[],"title_canon_sha256":"4ea1cbd3d5e7ff9528983ff5b8a328fa57f1cde34fa807d104f416c306262fb4","abstract_canon_sha256":"44ebbd9318c3df8ae6cb2b68590bdfea9cec6200ef254461f0410239cecce4b3"},"schema_version":"1.0"},"canonical_sha256":"5c2413717a0ee69800d79c5dc95e2bd8fdb48fabdaf1b19cff5aeefdea38f3c2","source":{"kind":"arxiv","id":"1811.01526","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.01526","created_at":"2026-05-18T00:01:34Z"},{"alias_kind":"arxiv_version","alias_value":"1811.01526v1","created_at":"2026-05-18T00:01:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.01526","created_at":"2026-05-18T00:01:34Z"},{"alias_kind":"pith_short_12","alias_value":"LQSBG4L2B3TJ","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LQSBG4L2B3TJQAGX","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LQSBG4L2","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:LQSBG4L2B3TJQAGXTRO4SXRL3D","target":"record","payload":{"canonical_record":{"source":{"id":"1811.01526","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-11-05T06:11:24Z","cross_cats_sorted":[],"title_canon_sha256":"4ea1cbd3d5e7ff9528983ff5b8a328fa57f1cde34fa807d104f416c306262fb4","abstract_canon_sha256":"44ebbd9318c3df8ae6cb2b68590bdfea9cec6200ef254461f0410239cecce4b3"},"schema_version":"1.0"},"canonical_sha256":"5c2413717a0ee69800d79c5dc95e2bd8fdb48fabdaf1b19cff5aeefdea38f3c2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:34.079127Z","signature_b64":"HWNYMlH470T9WEbf85Hk1aen1KApMg8ExmC6+tEFliZKVxGo120+9X9iV/IUziEFP8s95wDAnZi3YcSSCSBdDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c2413717a0ee69800d79c5dc95e2bd8fdb48fabdaf1b19cff5aeefdea38f3c2","last_reissued_at":"2026-05-18T00:01:34.078636Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:34.078636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.01526","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":"2026-05-18T00:01:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mtY6tjrKTA3s22vspnczUi6LcMNdpAPKv/+kYvD+2zAffcMkGgPFX6BdkPiyuCclQF3LLL2Pmg2tqa+sO0FRCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T04:22:28.678683Z"},"content_sha256":"d44f2732ae318d7975f49871ed23ab32a7d0aaeb58a83cea216de11f0c963285","schema_version":"1.0","event_id":"sha256:d44f2732ae318d7975f49871ed23ab32a7d0aaeb58a83cea216de11f0c963285"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:LQSBG4L2B3TJQAGXTRO4SXRL3D","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised RGBD Video Object Segmentation Using GANs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arif Mahmood, Maryam Sultana, Sajid Javed, Soon Ki Jung","submitted_at":"2018-11-05T06:11:24Z","abstract_excerpt":"Video object segmentation is a fundamental step in many advanced vision applications. Most existing algorithms are based on handcrafted features such as HOG, super-pixel segmentation or texture-based techniques, while recently deep features have been found to be more efficient. Existing algorithms observe performance degradation in the presence of challenges such as illumination variations, shadows, and color camouflage. To handle these challenges we propose a fusion based moving object segmentation algorithm which exploits color as well as depth information using GAN to achieve more accuracy."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.01526","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":"2026-05-18T00:01:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lFXkx0J9I0G4V3BxPTU4k86dloMzxwxnOjWN1r3Cf8P8zcWlp27ZUOhStSW7bDJnGAGO6xuvXCxnLKJ4mkR/Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T04:22:28.679034Z"},"content_sha256":"955d9191c899161b5c4e5be417b0855e19724aa6286e2b81c0d7ea4b0b059629","schema_version":"1.0","event_id":"sha256:955d9191c899161b5c4e5be417b0855e19724aa6286e2b81c0d7ea4b0b059629"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LQSBG4L2B3TJQAGXTRO4SXRL3D/bundle.json","state_url":"https://pith.science/pith/LQSBG4L2B3TJQAGXTRO4SXRL3D/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LQSBG4L2B3TJQAGXTRO4SXRL3D/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":"2026-06-02T04:22:28Z","links":{"resolver":"https://pith.science/pith/LQSBG4L2B3TJQAGXTRO4SXRL3D","bundle":"https://pith.science/pith/LQSBG4L2B3TJQAGXTRO4SXRL3D/bundle.json","state":"https://pith.science/pith/LQSBG4L2B3TJQAGXTRO4SXRL3D/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LQSBG4L2B3TJQAGXTRO4SXRL3D/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:LQSBG4L2B3TJQAGXTRO4SXRL3D","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":"44ebbd9318c3df8ae6cb2b68590bdfea9cec6200ef254461f0410239cecce4b3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-11-05T06:11:24Z","title_canon_sha256":"4ea1cbd3d5e7ff9528983ff5b8a328fa57f1cde34fa807d104f416c306262fb4"},"schema_version":"1.0","source":{"id":"1811.01526","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.01526","created_at":"2026-05-18T00:01:34Z"},{"alias_kind":"arxiv_version","alias_value":"1811.01526v1","created_at":"2026-05-18T00:01:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.01526","created_at":"2026-05-18T00:01:34Z"},{"alias_kind":"pith_short_12","alias_value":"LQSBG4L2B3TJ","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LQSBG4L2B3TJQAGX","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LQSBG4L2","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:955d9191c899161b5c4e5be417b0855e19724aa6286e2b81c0d7ea4b0b059629","target":"graph","created_at":"2026-05-18T00:01:34Z","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":"Video object segmentation is a fundamental step in many advanced vision applications. Most existing algorithms are based on handcrafted features such as HOG, super-pixel segmentation or texture-based techniques, while recently deep features have been found to be more efficient. Existing algorithms observe performance degradation in the presence of challenges such as illumination variations, shadows, and color camouflage. To handle these challenges we propose a fusion based moving object segmentation algorithm which exploits color as well as depth information using GAN to achieve more accuracy.","authors_text":"Arif Mahmood, Maryam Sultana, Sajid Javed, Soon Ki Jung","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-11-05T06:11:24Z","title":"Unsupervised RGBD Video Object Segmentation Using GANs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.01526","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:d44f2732ae318d7975f49871ed23ab32a7d0aaeb58a83cea216de11f0c963285","target":"record","created_at":"2026-05-18T00:01:34Z","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":"44ebbd9318c3df8ae6cb2b68590bdfea9cec6200ef254461f0410239cecce4b3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-11-05T06:11:24Z","title_canon_sha256":"4ea1cbd3d5e7ff9528983ff5b8a328fa57f1cde34fa807d104f416c306262fb4"},"schema_version":"1.0","source":{"id":"1811.01526","kind":"arxiv","version":1}},"canonical_sha256":"5c2413717a0ee69800d79c5dc95e2bd8fdb48fabdaf1b19cff5aeefdea38f3c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5c2413717a0ee69800d79c5dc95e2bd8fdb48fabdaf1b19cff5aeefdea38f3c2","first_computed_at":"2026-05-18T00:01:34.078636Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:34.078636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HWNYMlH470T9WEbf85Hk1aen1KApMg8ExmC6+tEFliZKVxGo120+9X9iV/IUziEFP8s95wDAnZi3YcSSCSBdDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:34.079127Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.01526","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d44f2732ae318d7975f49871ed23ab32a7d0aaeb58a83cea216de11f0c963285","sha256:955d9191c899161b5c4e5be417b0855e19724aa6286e2b81c0d7ea4b0b059629"],"state_sha256":"8cabd7985f7ac8db82708b066d264d3dce3a22b1e800842b76f474542ae7eabe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z1insTm7XIQKFw3PqaRYz7c7qRTcKpLfukdUdQIxKQCMny8WWNHbH2SzSXp1oKKpZx3xb4P07GboH4SCetejDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T04:22:28.680966Z","bundle_sha256":"b045ad2e4ecaae4813135534f8c1bb08d2610b89822a6685e2a77bd72fbeb04d"}}