{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:XEVQ5VRLYMX3OTB5RB3ZTG6WHL","short_pith_number":"pith:XEVQ5VRL","canonical_record":{"source":{"id":"1710.02754","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-07T22:10:08Z","cross_cats_sorted":["cs.AI","cs.GR"],"title_canon_sha256":"49727d2e3d620ca224bd48d505b8e6a60300535f96468921ba517b6c1bdf3441","abstract_canon_sha256":"d1c750986bc8dd7c87b707dcedcc2413f3ccd37d94f807c59a02dc0d120f5e51"},"schema_version":"1.0"},"canonical_sha256":"b92b0ed62bc32fb74c3d8877999bd63ad57499e6026213b32e55567fb0485ac4","source":{"kind":"arxiv","id":"1710.02754","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.02754","created_at":"2026-05-18T00:33:28Z"},{"alias_kind":"arxiv_version","alias_value":"1710.02754v1","created_at":"2026-05-18T00:33:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.02754","created_at":"2026-05-18T00:33:28Z"},{"alias_kind":"pith_short_12","alias_value":"XEVQ5VRLYMX3","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"XEVQ5VRLYMX3OTB5","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"XEVQ5VRL","created_at":"2026-05-18T12:31:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:XEVQ5VRLYMX3OTB5RB3ZTG6WHL","target":"record","payload":{"canonical_record":{"source":{"id":"1710.02754","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-07T22:10:08Z","cross_cats_sorted":["cs.AI","cs.GR"],"title_canon_sha256":"49727d2e3d620ca224bd48d505b8e6a60300535f96468921ba517b6c1bdf3441","abstract_canon_sha256":"d1c750986bc8dd7c87b707dcedcc2413f3ccd37d94f807c59a02dc0d120f5e51"},"schema_version":"1.0"},"canonical_sha256":"b92b0ed62bc32fb74c3d8877999bd63ad57499e6026213b32e55567fb0485ac4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:33:28.937875Z","signature_b64":"ni+O+VLAx2QYxDq4DYqrAl9bJ0zdOQ1w22yokJTVIwsCsrRJ+tphK8UoySd2rtzCY0Qr8Lot/7QvPuw3kbggCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b92b0ed62bc32fb74c3d8877999bd63ad57499e6026213b32e55567fb0485ac4","last_reissued_at":"2026-05-18T00:33:28.937169Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:33:28.937169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.02754","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":"2026-05-18T00:33:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IJBwUqJTSYK527l3n3QX96d2X6ytDioEayruXD138nAGCn3OanKTwZ7samv+iPz9po0Z8nQZNzJoJE46CjQCDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T02:34:55.124741Z"},"content_sha256":"ae6044dbc4fdc9fbad7e01d78b2fd53f33f3eddc61244f0e310d08b6baed17cd","schema_version":"1.0","event_id":"sha256:ae6044dbc4fdc9fbad7e01d78b2fd53f33f3eddc61244f0e310d08b6baed17cd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:XEVQ5VRLYMX3OTB5RB3ZTG6WHL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Texture Fuzzy Segmentation using Skew Divergence Adaptive Affinity Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR"],"primary_cat":"cs.CV","authors_text":"Bruno M. Carvalho, Edgar Gardu\\~no, Jos\\'e F. S. Neto, Matheus A. Gadelha, Tiago S. Santos, Waldson P. N. Leandro","submitted_at":"2017-10-07T22:10:08Z","abstract_excerpt":"Digital image segmentation is the process of assigning distinct labels to different objects in a digital image, and the fuzzy segmentation algorithm has been successfully used in the segmentation of images from a wide variety of sources. However, the traditional fuzzy segmentation algorithm fails to segment objects that are characterized by textures whose patterns cannot be successfully described by simple statistics computed over a very restricted area. In this paper, we propose an extension of the fuzzy segmentation algorithm that uses adaptive textural affinity functions to perform the segm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.02754","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":"2026-05-18T00:33:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9ZnuqjzqX2g9iTa3271iMSzj/lC6zuyo1QrVn5Bl3Bi0Vya//vU+4gY2LultCiLzIAgy7NMlyAIxngVl/UGcBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T02:34:55.125078Z"},"content_sha256":"c868d1b9ac1aaa5f1266385af1eda8f62cff0449f22892cc7988a63c0bb495fa","schema_version":"1.0","event_id":"sha256:c868d1b9ac1aaa5f1266385af1eda8f62cff0449f22892cc7988a63c0bb495fa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XEVQ5VRLYMX3OTB5RB3ZTG6WHL/bundle.json","state_url":"https://pith.science/pith/XEVQ5VRLYMX3OTB5RB3ZTG6WHL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XEVQ5VRLYMX3OTB5RB3ZTG6WHL/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":"2026-07-05T02:34:55Z","links":{"resolver":"https://pith.science/pith/XEVQ5VRLYMX3OTB5RB3ZTG6WHL","bundle":"https://pith.science/pith/XEVQ5VRLYMX3OTB5RB3ZTG6WHL/bundle.json","state":"https://pith.science/pith/XEVQ5VRLYMX3OTB5RB3ZTG6WHL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XEVQ5VRLYMX3OTB5RB3ZTG6WHL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:XEVQ5VRLYMX3OTB5RB3ZTG6WHL","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":"d1c750986bc8dd7c87b707dcedcc2413f3ccd37d94f807c59a02dc0d120f5e51","cross_cats_sorted":["cs.AI","cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-07T22:10:08Z","title_canon_sha256":"49727d2e3d620ca224bd48d505b8e6a60300535f96468921ba517b6c1bdf3441"},"schema_version":"1.0","source":{"id":"1710.02754","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.02754","created_at":"2026-05-18T00:33:28Z"},{"alias_kind":"arxiv_version","alias_value":"1710.02754v1","created_at":"2026-05-18T00:33:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.02754","created_at":"2026-05-18T00:33:28Z"},{"alias_kind":"pith_short_12","alias_value":"XEVQ5VRLYMX3","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"XEVQ5VRLYMX3OTB5","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"XEVQ5VRL","created_at":"2026-05-18T12:31:53Z"}],"graph_snapshots":[{"event_id":"sha256:c868d1b9ac1aaa5f1266385af1eda8f62cff0449f22892cc7988a63c0bb495fa","target":"graph","created_at":"2026-05-18T00:33:28Z","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":"Digital image segmentation is the process of assigning distinct labels to different objects in a digital image, and the fuzzy segmentation algorithm has been successfully used in the segmentation of images from a wide variety of sources. However, the traditional fuzzy segmentation algorithm fails to segment objects that are characterized by textures whose patterns cannot be successfully described by simple statistics computed over a very restricted area. In this paper, we propose an extension of the fuzzy segmentation algorithm that uses adaptive textural affinity functions to perform the segm","authors_text":"Bruno M. Carvalho, Edgar Gardu\\~no, Jos\\'e F. S. Neto, Matheus A. Gadelha, Tiago S. Santos, Waldson P. N. Leandro","cross_cats":["cs.AI","cs.GR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-07T22:10:08Z","title":"Texture Fuzzy Segmentation using Skew Divergence Adaptive Affinity Functions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.02754","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:ae6044dbc4fdc9fbad7e01d78b2fd53f33f3eddc61244f0e310d08b6baed17cd","target":"record","created_at":"2026-05-18T00:33:28Z","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":"d1c750986bc8dd7c87b707dcedcc2413f3ccd37d94f807c59a02dc0d120f5e51","cross_cats_sorted":["cs.AI","cs.GR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-07T22:10:08Z","title_canon_sha256":"49727d2e3d620ca224bd48d505b8e6a60300535f96468921ba517b6c1bdf3441"},"schema_version":"1.0","source":{"id":"1710.02754","kind":"arxiv","version":1}},"canonical_sha256":"b92b0ed62bc32fb74c3d8877999bd63ad57499e6026213b32e55567fb0485ac4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b92b0ed62bc32fb74c3d8877999bd63ad57499e6026213b32e55567fb0485ac4","first_computed_at":"2026-05-18T00:33:28.937169Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:33:28.937169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ni+O+VLAx2QYxDq4DYqrAl9bJ0zdOQ1w22yokJTVIwsCsrRJ+tphK8UoySd2rtzCY0Qr8Lot/7QvPuw3kbggCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:33:28.937875Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.02754","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae6044dbc4fdc9fbad7e01d78b2fd53f33f3eddc61244f0e310d08b6baed17cd","sha256:c868d1b9ac1aaa5f1266385af1eda8f62cff0449f22892cc7988a63c0bb495fa"],"state_sha256":"4f51d45579f4d6d2f1ea0443d29161961377c3d41b26e600d3f0228f2dbb9c5f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2Nl3V+7J98Z9xvcDtAmexKlO2E0eSc0xyuyQRpi2mibw+3FMfQ+qTMUTFSWgmMfaIjnql4dq34ditGLzaZuJBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T02:34:55.127119Z","bundle_sha256":"32af3cff54d610d44e7ca3973cddbff1309d486a4ad8fcbf358638580bea00f6"}}