{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UVMJHMBKML4EJXQF6YIIV7A7ZS","short_pith_number":"pith:UVMJHMBK","schema_version":"1.0","canonical_sha256":"a55893b02a62f844de05f6108afc1fccb940c4ba208e7de5e546bf23319a4b82","source":{"kind":"arxiv","id":"2504.04435","version":1},"attestation_state":"computed","paper":{"title":"Evaluation framework for Image Segmentation Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bharani Jayakumar, Tatiana Merkulova","submitted_at":"2025-04-06T10:20:26Z","abstract_excerpt":"This paper presents a comprehensive evaluation framework for image segmentation algorithms, encompassing naive methods, machine learning approaches, and deep learning techniques. We begin by introducing the fundamental concepts and importance of image segmentation, and the role of interactive segmentation in enhancing accuracy. A detailed background theory section explores various segmentation methods, including thresholding, edge detection, region growing, feature extraction, random forests, support vector machines, convolutional neural networks, U-Net, and Mask R-CNN. The implementation and "},"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":"2504.04435","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-06T10:20:26Z","cross_cats_sorted":[],"title_canon_sha256":"3e5d439fcee075aaab674ba19232f272d42ccb8e8c01bb9ea3d115ff07530947","abstract_canon_sha256":"0fd3c5bb7e19f4cecfc5c24e0189d8dd0853d2748cd7624e7203bc6f48aef985"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:45:23.808593Z","signature_b64":"FSGFgpEpbdQpx7qUqtbXzVhoQHIVKNSw0oCpAh8VD1Ek0Qyr4WPKNiGHqALOJJUqpheTPAaA7a25iHmdBpGyAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a55893b02a62f844de05f6108afc1fccb940c4ba208e7de5e546bf23319a4b82","last_reissued_at":"2026-07-05T10:45:23.808184Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:45:23.808184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluation framework for Image Segmentation Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bharani Jayakumar, Tatiana Merkulova","submitted_at":"2025-04-06T10:20:26Z","abstract_excerpt":"This paper presents a comprehensive evaluation framework for image segmentation algorithms, encompassing naive methods, machine learning approaches, and deep learning techniques. We begin by introducing the fundamental concepts and importance of image segmentation, and the role of interactive segmentation in enhancing accuracy. A detailed background theory section explores various segmentation methods, including thresholding, edge detection, region growing, feature extraction, random forests, support vector machines, convolutional neural networks, U-Net, and Mask R-CNN. The implementation and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.04435","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/2504.04435/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":"2504.04435","created_at":"2026-07-05T10:45:23.808248+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.04435v1","created_at":"2026-07-05T10:45:23.808248+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.04435","created_at":"2026-07-05T10:45:23.808248+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVMJHMBKML4E","created_at":"2026-07-05T10:45:23.808248+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVMJHMBKML4EJXQF","created_at":"2026-07-05T10:45:23.808248+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVMJHMBK","created_at":"2026-07-05T10:45:23.808248+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/UVMJHMBKML4EJXQF6YIIV7A7ZS","json":"https://pith.science/pith/UVMJHMBKML4EJXQF6YIIV7A7ZS.json","graph_json":"https://pith.science/api/pith-number/UVMJHMBKML4EJXQF6YIIV7A7ZS/graph.json","events_json":"https://pith.science/api/pith-number/UVMJHMBKML4EJXQF6YIIV7A7ZS/events.json","paper":"https://pith.science/paper/UVMJHMBK"},"agent_actions":{"view_html":"https://pith.science/pith/UVMJHMBKML4EJXQF6YIIV7A7ZS","download_json":"https://pith.science/pith/UVMJHMBKML4EJXQF6YIIV7A7ZS.json","view_paper":"https://pith.science/paper/UVMJHMBK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.04435&json=true","fetch_graph":"https://pith.science/api/pith-number/UVMJHMBKML4EJXQF6YIIV7A7ZS/graph.json","fetch_events":"https://pith.science/api/pith-number/UVMJHMBKML4EJXQF6YIIV7A7ZS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVMJHMBKML4EJXQF6YIIV7A7ZS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVMJHMBKML4EJXQF6YIIV7A7ZS/action/storage_attestation","attest_author":"https://pith.science/pith/UVMJHMBKML4EJXQF6YIIV7A7ZS/action/author_attestation","sign_citation":"https://pith.science/pith/UVMJHMBKML4EJXQF6YIIV7A7ZS/action/citation_signature","submit_replication":"https://pith.science/pith/UVMJHMBKML4EJXQF6YIIV7A7ZS/action/replication_record"}},"created_at":"2026-07-05T10:45:23.808248+00:00","updated_at":"2026-07-05T10:45:23.808248+00:00"}