{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:YZBQRGSNQOZHE6OMA4DISQHTXT","short_pith_number":"pith:YZBQRGSN","schema_version":"1.0","canonical_sha256":"c643089a4d83b27279cc07068940f3bce33601fc89b389482ac1d66bf5eb406f","source":{"kind":"arxiv","id":"1710.02726","version":1},"attestation_state":"computed","paper":{"title":"Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ebrahim Karami, Mohamed Shehata, Siva Prasad","submitted_at":"2017-10-07T19:22:59Z","abstract_excerpt":"Fast and robust image matching is a very important task with various applications in computer vision and robotics. In this paper, we compare the performance of three different image matching techniques, i.e., SIFT, SURF, and ORB, against different kinds of transformations and deformations such as scaling, rotation, noise, fish eye distortion, and shearing. For this purpose, we manually apply different types of transformations on original images and compute the matching evaluation parameters such as the number of key points in images, the matching rate, and the execution time required for each "},"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":"1710.02726","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-07T19:22:59Z","cross_cats_sorted":[],"title_canon_sha256":"18b7d8e4a77da67e07e284047bb34a280169dd250cd0b325616575ec20aa3837","abstract_canon_sha256":"725a34720c656faf0473a34f2e45f2325f7608bdcee3b54058fdd56a5afd5d3a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:33:29.023592Z","signature_b64":"QKWsgSwFwkxqkA1XUp7NRAEMxTaNh/qopgsWwzuLMSqDg5+mD/k5D9pt189g+gxtdqIKMkGzEVpDehPJV9C0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c643089a4d83b27279cc07068940f3bce33601fc89b389482ac1d66bf5eb406f","last_reissued_at":"2026-05-18T00:33:29.023155Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:33:29.023155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ebrahim Karami, Mohamed Shehata, Siva Prasad","submitted_at":"2017-10-07T19:22:59Z","abstract_excerpt":"Fast and robust image matching is a very important task with various applications in computer vision and robotics. In this paper, we compare the performance of three different image matching techniques, i.e., SIFT, SURF, and ORB, against different kinds of transformations and deformations such as scaling, rotation, noise, fish eye distortion, and shearing. For this purpose, we manually apply different types of transformations on original images and compute the matching evaluation parameters such as the number of key points in images, the matching rate, and the execution time required for each "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.02726","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1710.02726","created_at":"2026-05-18T00:33:29.023216+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.02726v1","created_at":"2026-05-18T00:33:29.023216+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.02726","created_at":"2026-05-18T00:33:29.023216+00:00"},{"alias_kind":"pith_short_12","alias_value":"YZBQRGSNQOZH","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"YZBQRGSNQOZHE6OM","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"YZBQRGSN","created_at":"2026-05-18T12:31:59.375834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2510.17422","citing_title":"DeepDetect: Learning All-in-One Dense Keypoints","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YZBQRGSNQOZHE6OMA4DISQHTXT","json":"https://pith.science/pith/YZBQRGSNQOZHE6OMA4DISQHTXT.json","graph_json":"https://pith.science/api/pith-number/YZBQRGSNQOZHE6OMA4DISQHTXT/graph.json","events_json":"https://pith.science/api/pith-number/YZBQRGSNQOZHE6OMA4DISQHTXT/events.json","paper":"https://pith.science/paper/YZBQRGSN"},"agent_actions":{"view_html":"https://pith.science/pith/YZBQRGSNQOZHE6OMA4DISQHTXT","download_json":"https://pith.science/pith/YZBQRGSNQOZHE6OMA4DISQHTXT.json","view_paper":"https://pith.science/paper/YZBQRGSN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.02726&json=true","fetch_graph":"https://pith.science/api/pith-number/YZBQRGSNQOZHE6OMA4DISQHTXT/graph.json","fetch_events":"https://pith.science/api/pith-number/YZBQRGSNQOZHE6OMA4DISQHTXT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YZBQRGSNQOZHE6OMA4DISQHTXT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YZBQRGSNQOZHE6OMA4DISQHTXT/action/storage_attestation","attest_author":"https://pith.science/pith/YZBQRGSNQOZHE6OMA4DISQHTXT/action/author_attestation","sign_citation":"https://pith.science/pith/YZBQRGSNQOZHE6OMA4DISQHTXT/action/citation_signature","submit_replication":"https://pith.science/pith/YZBQRGSNQOZHE6OMA4DISQHTXT/action/replication_record"}},"created_at":"2026-05-18T00:33:29.023216+00:00","updated_at":"2026-05-18T00:33:29.023216+00:00"}