{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:QLI7KXNO7E5ODU3VXLVZXMUK55","short_pith_number":"pith:QLI7KXNO","canonical_record":{"source":{"id":"1605.09116","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2016-05-30T06:50:31Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"2be1118d66b77902c99a33976119e2fcb99a5af0d3b004004dee9102a86a22a6","abstract_canon_sha256":"407b59556ee65e2720ace6f905182ab0ab2e171b1b930503f298904a75412b42"},"schema_version":"1.0"},"canonical_sha256":"82d1f55daef93ae1d375baeb9bb28aef728b744be5b91f6e3f35670dd3f07785","source":{"kind":"arxiv","id":"1605.09116","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1605.09116","created_at":"2026-05-18T01:13:22Z"},{"alias_kind":"arxiv_version","alias_value":"1605.09116v1","created_at":"2026-05-18T01:13:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.09116","created_at":"2026-05-18T01:13:22Z"},{"alias_kind":"pith_short_12","alias_value":"QLI7KXNO7E5O","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_16","alias_value":"QLI7KXNO7E5ODU3V","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_8","alias_value":"QLI7KXNO","created_at":"2026-05-18T12:30:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:QLI7KXNO7E5ODU3VXLVZXMUK55","target":"record","payload":{"canonical_record":{"source":{"id":"1605.09116","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2016-05-30T06:50:31Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"2be1118d66b77902c99a33976119e2fcb99a5af0d3b004004dee9102a86a22a6","abstract_canon_sha256":"407b59556ee65e2720ace6f905182ab0ab2e171b1b930503f298904a75412b42"},"schema_version":"1.0"},"canonical_sha256":"82d1f55daef93ae1d375baeb9bb28aef728b744be5b91f6e3f35670dd3f07785","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:13:22.061363Z","signature_b64":"U6qaN0ttmCZ6UWN/0VpAWTTu3MBqLIJWGx2XPrykkiqRtL3Pnn6+pGDi9VnhmTzJrEEivg/2JF89NrXdJpNvDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82d1f55daef93ae1d375baeb9bb28aef728b744be5b91f6e3f35670dd3f07785","last_reissued_at":"2026-05-18T01:13:22.060768Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:13:22.060768Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1605.09116","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":"2026-05-18T01:13:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EB5CQpUo48TwQpwbjpsYx9ZcGMcqhF1c/RleWFZAWigSt6vHdmh2XnOlk1u1zRRBTG1D1gP43X33XvGPuv/3Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T06:23:07.034202Z"},"content_sha256":"9f001b5ae09748f2bd1d3a317c67af3b41c81707a7a54cd62c91d59cccc94457","schema_version":"1.0","event_id":"sha256:9f001b5ae09748f2bd1d3a317c67af3b41c81707a7a54cd62c91d59cccc94457"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:QLI7KXNO7E5ODU3VXLVZXMUK55","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Image segmentation based on the hybrid total variation model and the K-means clustering strategy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"math.OC","authors_text":"Baoli Shi, Jing Xu, Zhi-Feng Pang","submitted_at":"2016-05-30T06:50:31Z","abstract_excerpt":"The performance of image segmentation highly relies on the original inputting image. When the image is contaminated by some noises or blurs, we can not obtain the efficient segmentation result by using direct segmentation methods. In order to efficiently segment the contaminated image, this paper proposes a two step method based on the hybrid total variation model with a box constraint and the K-means clustering method. In the first step, the hybrid model is based on the weighted convex combination between the total variation functional and the high-order total variation as the regularization "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.09116","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":"2026-05-18T01:13:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tZCTUonwZW1rPmlwAtAIqQiEk6/NqMcAAiHa7EHDwrY4deb93jG9vjt5G7W9YGK4PpAPLu5Yn1fYmykVcPveCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T06:23:07.034931Z"},"content_sha256":"889b651535c77d6b690f6ca8f598df475d4e920e3a89eca89745b97c40f59cac","schema_version":"1.0","event_id":"sha256:889b651535c77d6b690f6ca8f598df475d4e920e3a89eca89745b97c40f59cac"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QLI7KXNO7E5ODU3VXLVZXMUK55/bundle.json","state_url":"https://pith.science/pith/QLI7KXNO7E5ODU3VXLVZXMUK55/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QLI7KXNO7E5ODU3VXLVZXMUK55/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":"2026-06-06T06:23:07Z","links":{"resolver":"https://pith.science/pith/QLI7KXNO7E5ODU3VXLVZXMUK55","bundle":"https://pith.science/pith/QLI7KXNO7E5ODU3VXLVZXMUK55/bundle.json","state":"https://pith.science/pith/QLI7KXNO7E5ODU3VXLVZXMUK55/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QLI7KXNO7E5ODU3VXLVZXMUK55/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:QLI7KXNO7E5ODU3VXLVZXMUK55","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":"407b59556ee65e2720ace6f905182ab0ab2e171b1b930503f298904a75412b42","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2016-05-30T06:50:31Z","title_canon_sha256":"2be1118d66b77902c99a33976119e2fcb99a5af0d3b004004dee9102a86a22a6"},"schema_version":"1.0","source":{"id":"1605.09116","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1605.09116","created_at":"2026-05-18T01:13:22Z"},{"alias_kind":"arxiv_version","alias_value":"1605.09116v1","created_at":"2026-05-18T01:13:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.09116","created_at":"2026-05-18T01:13:22Z"},{"alias_kind":"pith_short_12","alias_value":"QLI7KXNO7E5O","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_16","alias_value":"QLI7KXNO7E5ODU3V","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_8","alias_value":"QLI7KXNO","created_at":"2026-05-18T12:30:39Z"}],"graph_snapshots":[{"event_id":"sha256:889b651535c77d6b690f6ca8f598df475d4e920e3a89eca89745b97c40f59cac","target":"graph","created_at":"2026-05-18T01:13:22Z","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":"The performance of image segmentation highly relies on the original inputting image. When the image is contaminated by some noises or blurs, we can not obtain the efficient segmentation result by using direct segmentation methods. In order to efficiently segment the contaminated image, this paper proposes a two step method based on the hybrid total variation model with a box constraint and the K-means clustering method. In the first step, the hybrid model is based on the weighted convex combination between the total variation functional and the high-order total variation as the regularization ","authors_text":"Baoli Shi, Jing Xu, Zhi-Feng Pang","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2016-05-30T06:50:31Z","title":"Image segmentation based on the hybrid total variation model and the K-means clustering strategy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.09116","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":"sha256:9f001b5ae09748f2bd1d3a317c67af3b41c81707a7a54cd62c91d59cccc94457","target":"record","created_at":"2026-05-18T01:13:22Z","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":"407b59556ee65e2720ace6f905182ab0ab2e171b1b930503f298904a75412b42","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2016-05-30T06:50:31Z","title_canon_sha256":"2be1118d66b77902c99a33976119e2fcb99a5af0d3b004004dee9102a86a22a6"},"schema_version":"1.0","source":{"id":"1605.09116","kind":"arxiv","version":1}},"canonical_sha256":"82d1f55daef93ae1d375baeb9bb28aef728b744be5b91f6e3f35670dd3f07785","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"82d1f55daef93ae1d375baeb9bb28aef728b744be5b91f6e3f35670dd3f07785","first_computed_at":"2026-05-18T01:13:22.060768Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:13:22.060768Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"U6qaN0ttmCZ6UWN/0VpAWTTu3MBqLIJWGx2XPrykkiqRtL3Pnn6+pGDi9VnhmTzJrEEivg/2JF89NrXdJpNvDw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:13:22.061363Z","signed_message":"canonical_sha256_bytes"},"source_id":"1605.09116","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9f001b5ae09748f2bd1d3a317c67af3b41c81707a7a54cd62c91d59cccc94457","sha256:889b651535c77d6b690f6ca8f598df475d4e920e3a89eca89745b97c40f59cac"],"state_sha256":"d4fbb768c662ff601f1e7a502eeb9bd95cee45fe0b0d708d9b63bf2e37914a60"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QUgTdzImHRj2+vZbSc4OLkDGS5jj75PAjhrMeqGnmBshiifehcaIv4yIVThr9Yi1eKRLOONDSpf5ds3MCAbLCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T06:23:07.038801Z","bundle_sha256":"2e459a302123f83bd262821625914016669faa65d46f9b8477b68090c0415bcf"}}