{"id":"W2054399986","doi":"10.1364/ao.43.000304","title":"Constrained quadratic correlation filters for target detection","year":2004,"lang":"en","type":"article","venue":"Applied Optics","topic":"Optical and Acousto-Optic Technologies","field":"Physics and Astronomy","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Linear filter; Synthetic aperture radar; Artificial intelligence; Pattern recognition (psychology); Metric (unit); Quadratic equation; Quadratic function; Invariant (physics); Filter (signal processing); Automatic target recognition; Computer vision; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008432423,0.0006426953,0.0004446821,0.000553873,0.0003743869,0.0006782684,0.0007498515,0.0006884135,0.004081214],"category_scores_gemma":[0.002338221,0.0003232278,0.0005040197,0.0008506309,0.0004507676,0.0008516893,0.0005637739,0.0009447753,0.001172338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007401758,"about_ca_system_score_gemma":0.0009308406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002796934,"about_ca_topic_score_gemma":0.004018167,"domain_scores_codex":[0.9993647,0.000146819,0.00002268994,0.00009000497,0.0003246989,0.00005106664],"domain_scores_gemma":[0.9993048,0.0002902436,0.00005582911,0.00009097322,0.0002287872,0.00002930912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002093992,0.00009687999,0.0005554706,0.0002187714,0.00006555978,0.0001114325,0.00008997697,0.2784869,0.05767599,0.1064931,0.00837119,0.5476253],"study_design_scores_gemma":[0.00001522933,0.00007106984,0.000241198,0.00001306486,0.0000123731,0.00009139483,0.00001040009,0.9634439,0.01237187,0.01275656,0.01095277,0.00002011123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001137074,0.0001064427,0.9978058,0.00004021645,0.00001778559,0.000009595549,0.00001856341,0.0001461187,0.0007184841],"genre_scores_gemma":[0.06919644,0.0003330673,0.9255123,0.00008971859,0.00005985856,0.00008522119,0.0001542244,0.00008955873,0.004479693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004081214,"threshold_uncertainty_score":0.01365304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006804370346392022,"score_gpt":0.211320501934354,"score_spread":0.204516131587962,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}