{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DFWVDGWPEP3DS5YDX74734SGK4","short_pith_number":"pith:DFWVDGWP","schema_version":"1.0","canonical_sha256":"196d519acf23f6397703bff9fdf246571b7fe62155db428d52def0ce580a5ab9","source":{"kind":"arxiv","id":"1805.10421","version":2},"attestation_state":"computed","paper":{"title":"Enhanced-alignment Measure for Binary Foreground Map Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ali Borji, Bo Ren, Cheng Gong, Deng-Ping Fan, Ming-Ming Cheng, Yang Cao","submitted_at":"2018-05-26T03:29:38Z","abstract_excerpt":"The existing binary foreground map (FM) measures to address various types of errors in either pixel-wise or structural ways. These measures consider pixel-level match or image-level information independently, while cognitive vision studies have shown that human vision is highly sensitive to both global information and local details in scenes. In this paper, we take a detailed look at current binary FM evaluation measures and propose a novel and effective E-measure (Enhanced-alignment measure). Our measure combines local pixel values with the image-level mean value in one term, jointly capturin"},"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":"1805.10421","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-26T03:29:38Z","cross_cats_sorted":[],"title_canon_sha256":"02fcf97692c93be2ac067e67797d11952af5e50eba01ae8c5a1e91456c01493e","abstract_canon_sha256":"145ac998f00f89ecfff5e2119397ca11c4bbf1b12e581c09930148e328d1a3c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:00:50.546245Z","signature_b64":"eEE0LTkXoKxZ+yS+HZblZYlqcyH9D+ZN9Ovth6+7594EYtuH0zn4rlQpxu/tRFdD06xHlawadI6qv7TIdwcyBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"196d519acf23f6397703bff9fdf246571b7fe62155db428d52def0ce580a5ab9","last_reissued_at":"2026-07-05T00:00:50.545700Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:00:50.545700Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhanced-alignment Measure for Binary Foreground Map Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ali Borji, Bo Ren, Cheng Gong, Deng-Ping Fan, Ming-Ming Cheng, Yang Cao","submitted_at":"2018-05-26T03:29:38Z","abstract_excerpt":"The existing binary foreground map (FM) measures to address various types of errors in either pixel-wise or structural ways. These measures consider pixel-level match or image-level information independently, while cognitive vision studies have shown that human vision is highly sensitive to both global information and local details in scenes. In this paper, we take a detailed look at current binary FM evaluation measures and propose a novel and effective E-measure (Enhanced-alignment measure). Our measure combines local pixel values with the image-level mean value in one term, jointly capturin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10421","kind":"arxiv","version":2},"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/1805.10421/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":"1805.10421","created_at":"2026-07-05T00:00:50.545763+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.10421v2","created_at":"2026-07-05T00:00:50.545763+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10421","created_at":"2026-07-05T00:00:50.545763+00:00"},{"alias_kind":"pith_short_12","alias_value":"DFWVDGWPEP3D","created_at":"2026-07-05T00:00:50.545763+00:00"},{"alias_kind":"pith_short_16","alias_value":"DFWVDGWPEP3DS5YD","created_at":"2026-07-05T00:00:50.545763+00:00"},{"alias_kind":"pith_short_8","alias_value":"DFWVDGWP","created_at":"2026-07-05T00:00:50.545763+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20161","citing_title":"ARTEMIS: Agent-guided Reliability-aware Temporal Mask Evolution for Imperfectly Supervised Video Polyp Segmentation","ref_index":63,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08906","citing_title":"DifferSeg: Towards Diverse Multimodal Binary Segmentation via Differential Perception and Frequency Guidance","ref_index":99,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31496","citing_title":"HVPNet: A Bio-Inspired Network for General Salient and Camouflaged Object Detection","ref_index":87,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02935","citing_title":"Modality-Specific Hierarchical Enhancement for RGB-D Camouflaged Object Detection","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10894","citing_title":"EviRCOD: Evidence-Guided Probabilistic Decoding for Referring Camouflaged Object Detection","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07146","citing_title":"UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17879","citing_title":"Exploring Boundary-Aware Spatial-Frequency Fusion for Camouflaged Object Detection","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16582","citing_title":"Camo-M3FD: A New Benchmark Dataset for Cross-Spectral Camouflaged Pedestrian Detection","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16854","citing_title":"CATP: Confidence-Aware Token Pruning for Camouflaged Object Detection","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DFWVDGWPEP3DS5YDX74734SGK4","json":"https://pith.science/pith/DFWVDGWPEP3DS5YDX74734SGK4.json","graph_json":"https://pith.science/api/pith-number/DFWVDGWPEP3DS5YDX74734SGK4/graph.json","events_json":"https://pith.science/api/pith-number/DFWVDGWPEP3DS5YDX74734SGK4/events.json","paper":"https://pith.science/paper/DFWVDGWP"},"agent_actions":{"view_html":"https://pith.science/pith/DFWVDGWPEP3DS5YDX74734SGK4","download_json":"https://pith.science/pith/DFWVDGWPEP3DS5YDX74734SGK4.json","view_paper":"https://pith.science/paper/DFWVDGWP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.10421&json=true","fetch_graph":"https://pith.science/api/pith-number/DFWVDGWPEP3DS5YDX74734SGK4/graph.json","fetch_events":"https://pith.science/api/pith-number/DFWVDGWPEP3DS5YDX74734SGK4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DFWVDGWPEP3DS5YDX74734SGK4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DFWVDGWPEP3DS5YDX74734SGK4/action/storage_attestation","attest_author":"https://pith.science/pith/DFWVDGWPEP3DS5YDX74734SGK4/action/author_attestation","sign_citation":"https://pith.science/pith/DFWVDGWPEP3DS5YDX74734SGK4/action/citation_signature","submit_replication":"https://pith.science/pith/DFWVDGWPEP3DS5YDX74734SGK4/action/replication_record"}},"created_at":"2026-07-05T00:00:50.545763+00:00","updated_at":"2026-07-05T00:00:50.545763+00:00"}