{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:NIXWRWFK7HHKNQ5OJ6HQICTLMZ","short_pith_number":"pith:NIXWRWFK","schema_version":"1.0","canonical_sha256":"6a2f68d8aaf9cea6c3ae4f8f040a6b664f725039b2550cd35884dd799fb39076","source":{"kind":"arxiv","id":"1603.06036","version":3},"attestation_state":"computed","paper":{"title":"Fractal Dimension Invariant Filtering and Its CNN-based Implementation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongteng Xu, Hongyuan Zha, Junchi Yan, Nils Persson, Weiyao Lin","submitted_at":"2016-03-19T03:29:57Z","abstract_excerpt":"Fractal analysis has been widely used in computer vision, especially in texture image processing and texture analysis. The key concept of fractal-based image model is the fractal dimension, which is invariant to bi-Lipschitz transformation of image, and thus capable of representing intrinsic structural information of image robustly. However, the invariance of fractal dimension generally does not hold after filtering, which limits the application of fractal-based image model. In this paper, we propose a novel fractal dimension invariant filtering (FDIF) method, extending the invariance of fract"},"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":"1603.06036","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-19T03:29:57Z","cross_cats_sorted":[],"title_canon_sha256":"81535cf35ea82fa922201807229b4577999df27a0cf271deec100f2e8e26c21f","abstract_canon_sha256":"68eadec7eae8f8a28400ef7a468c0a58500e113034c6ea6d2567e7fabecdb811"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:48:32.998033Z","signature_b64":"IcU92+2Dtw1wx/7QKFEvIxTjKS2a748Nb1H4qtZc5RZrlvwLOREKwVCyEFr3AHtTcJMkX3HRZT+LASQFLErsDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a2f68d8aaf9cea6c3ae4f8f040a6b664f725039b2550cd35884dd799fb39076","last_reissued_at":"2026-05-18T00:48:32.997234Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:48:32.997234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fractal Dimension Invariant Filtering and Its CNN-based Implementation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongteng Xu, Hongyuan Zha, Junchi Yan, Nils Persson, Weiyao Lin","submitted_at":"2016-03-19T03:29:57Z","abstract_excerpt":"Fractal analysis has been widely used in computer vision, especially in texture image processing and texture analysis. The key concept of fractal-based image model is the fractal dimension, which is invariant to bi-Lipschitz transformation of image, and thus capable of representing intrinsic structural information of image robustly. However, the invariance of fractal dimension generally does not hold after filtering, which limits the application of fractal-based image model. In this paper, we propose a novel fractal dimension invariant filtering (FDIF) method, extending the invariance of fract"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.06036","kind":"arxiv","version":3},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1603.06036","created_at":"2026-05-18T00:48:32.997387+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.06036v3","created_at":"2026-05-18T00:48:32.997387+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.06036","created_at":"2026-05-18T00:48:32.997387+00:00"},{"alias_kind":"pith_short_12","alias_value":"NIXWRWFK7HHK","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"NIXWRWFK7HHKNQ5O","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"NIXWRWFK","created_at":"2026-05-18T12:30:32.724797+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/NIXWRWFK7HHKNQ5OJ6HQICTLMZ","json":"https://pith.science/pith/NIXWRWFK7HHKNQ5OJ6HQICTLMZ.json","graph_json":"https://pith.science/api/pith-number/NIXWRWFK7HHKNQ5OJ6HQICTLMZ/graph.json","events_json":"https://pith.science/api/pith-number/NIXWRWFK7HHKNQ5OJ6HQICTLMZ/events.json","paper":"https://pith.science/paper/NIXWRWFK"},"agent_actions":{"view_html":"https://pith.science/pith/NIXWRWFK7HHKNQ5OJ6HQICTLMZ","download_json":"https://pith.science/pith/NIXWRWFK7HHKNQ5OJ6HQICTLMZ.json","view_paper":"https://pith.science/paper/NIXWRWFK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.06036&json=true","fetch_graph":"https://pith.science/api/pith-number/NIXWRWFK7HHKNQ5OJ6HQICTLMZ/graph.json","fetch_events":"https://pith.science/api/pith-number/NIXWRWFK7HHKNQ5OJ6HQICTLMZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NIXWRWFK7HHKNQ5OJ6HQICTLMZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NIXWRWFK7HHKNQ5OJ6HQICTLMZ/action/storage_attestation","attest_author":"https://pith.science/pith/NIXWRWFK7HHKNQ5OJ6HQICTLMZ/action/author_attestation","sign_citation":"https://pith.science/pith/NIXWRWFK7HHKNQ5OJ6HQICTLMZ/action/citation_signature","submit_replication":"https://pith.science/pith/NIXWRWFK7HHKNQ5OJ6HQICTLMZ/action/replication_record"}},"created_at":"2026-05-18T00:48:32.997387+00:00","updated_at":"2026-05-18T00:48:32.997387+00:00"}