{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:AFUAY7ERZC5YUOAGVOLOLYLCAK","short_pith_number":"pith:AFUAY7ER","schema_version":"1.0","canonical_sha256":"01680c7c91c8bb8a3806ab96e5e16202b3aaf96f7131cf22c208674964bb5917","source":{"kind":"arxiv","id":"2211.08460","version":1},"attestation_state":"computed","paper":{"title":"ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Agapito Ledezma, Arrate Mu\\~noz-Barrutia, Javier Pascau, Laura Nicol\\'as-S\\'aenz","submitted_at":"2022-11-15T19:26:51Z","abstract_excerpt":"In any computer vision task involving color images, a necessary step is classifying pixels according to color and segmenting the respective areas. However, the development of methods able to successfully complete this task has proven challenging, mainly due to the gap between human color perception, linguistic color terms, and digital representation. In this paper, we propose a novel method combining geometric analysis of color theory, fuzzy color spaces, and multi-label systems for the automatic classification of pixels according to 12 standard color categories (Green, Yellow, Light Orange, D"},"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":"2211.08460","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-15T19:26:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7b22f3f44d733bcdee818830657ebbb2e4ce77683e92d218d68419287664de48","abstract_canon_sha256":"e6b10de582118de9fd496b38b607e2852c3bf6e20ef7f81a49da25f6a5abc6fc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:16:40.195741Z","signature_b64":"EP83aqxeIRupvlV6DY2Osoy1KAdHRxUbEsqStlOjRrvnXOBPEbivexebiipLa8qN4OXbLed9v5qINqfzYQ0gAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01680c7c91c8bb8a3806ab96e5e16202b3aaf96f7131cf22c208674964bb5917","last_reissued_at":"2026-07-05T05:16:40.195354Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:16:40.195354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Agapito Ledezma, Arrate Mu\\~noz-Barrutia, Javier Pascau, Laura Nicol\\'as-S\\'aenz","submitted_at":"2022-11-15T19:26:51Z","abstract_excerpt":"In any computer vision task involving color images, a necessary step is classifying pixels according to color and segmenting the respective areas. However, the development of methods able to successfully complete this task has proven challenging, mainly due to the gap between human color perception, linguistic color terms, and digital representation. In this paper, we propose a novel method combining geometric analysis of color theory, fuzzy color spaces, and multi-label systems for the automatic classification of pixels according to 12 standard color categories (Green, Yellow, Light Orange, D"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.08460","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2211.08460/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2211.08460","created_at":"2026-07-05T05:16:40.195412+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.08460v1","created_at":"2026-07-05T05:16:40.195412+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.08460","created_at":"2026-07-05T05:16:40.195412+00:00"},{"alias_kind":"pith_short_12","alias_value":"AFUAY7ERZC5Y","created_at":"2026-07-05T05:16:40.195412+00:00"},{"alias_kind":"pith_short_16","alias_value":"AFUAY7ERZC5YUOAG","created_at":"2026-07-05T05:16:40.195412+00:00"},{"alias_kind":"pith_short_8","alias_value":"AFUAY7ER","created_at":"2026-07-05T05:16:40.195412+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/AFUAY7ERZC5YUOAGVOLOLYLCAK","json":"https://pith.science/pith/AFUAY7ERZC5YUOAGVOLOLYLCAK.json","graph_json":"https://pith.science/api/pith-number/AFUAY7ERZC5YUOAGVOLOLYLCAK/graph.json","events_json":"https://pith.science/api/pith-number/AFUAY7ERZC5YUOAGVOLOLYLCAK/events.json","paper":"https://pith.science/paper/AFUAY7ER"},"agent_actions":{"view_html":"https://pith.science/pith/AFUAY7ERZC5YUOAGVOLOLYLCAK","download_json":"https://pith.science/pith/AFUAY7ERZC5YUOAGVOLOLYLCAK.json","view_paper":"https://pith.science/paper/AFUAY7ER","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.08460&json=true","fetch_graph":"https://pith.science/api/pith-number/AFUAY7ERZC5YUOAGVOLOLYLCAK/graph.json","fetch_events":"https://pith.science/api/pith-number/AFUAY7ERZC5YUOAGVOLOLYLCAK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AFUAY7ERZC5YUOAGVOLOLYLCAK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AFUAY7ERZC5YUOAGVOLOLYLCAK/action/storage_attestation","attest_author":"https://pith.science/pith/AFUAY7ERZC5YUOAGVOLOLYLCAK/action/author_attestation","sign_citation":"https://pith.science/pith/AFUAY7ERZC5YUOAGVOLOLYLCAK/action/citation_signature","submit_replication":"https://pith.science/pith/AFUAY7ERZC5YUOAGVOLOLYLCAK/action/replication_record"}},"created_at":"2026-07-05T05:16:40.195412+00:00","updated_at":"2026-07-05T05:16:40.195412+00:00"}