{"id":"W2076161723","doi":"10.1364/ao.43.005198","title":"Quadratic correlation filter design methodology for target detection and surveillance applications","year":2004,"lang":"en","type":"article","venue":"Applied Optics","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Metric (unit); Rayleigh quotient; Linear filter; Quadratic equation; Artificial intelligence; Quadratic programming; Performance metric; Filter (signal processing); Pattern recognition (psychology); Matched filter; Computer vision; Mathematics; Mathematical optimization","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000662096,0.000151523,0.0001980806,0.00009743276,0.0001293314,0.00002784609,0.00007302966,0.0001767852,0.000005624951],"category_scores_gemma":[0.0001578513,0.0001645848,0.00003058199,0.0001723351,0.00005224467,0.00006523627,0.00001486431,0.0001502418,0.00001656734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007523252,"about_ca_system_score_gemma":0.00001411792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001128607,"about_ca_topic_score_gemma":0.000003258262,"domain_scores_codex":[0.9992151,0.00006156053,0.000238081,0.0002009604,0.00006801995,0.0002162669],"domain_scores_gemma":[0.9986691,0.0009938815,0.00005312324,0.0001895548,0.000052469,0.00004190477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003628028,0.000008422758,0.00001478076,0.00007072865,0.00003576348,2.440659e-7,0.0002423494,0.8422474,0.1370262,0.01003471,0.00001929906,0.01026379],"study_design_scores_gemma":[0.001332917,0.0001493365,0.0006943649,0.000005835006,0.00004546343,0.00003027965,0.0002599102,0.1827173,0.5402516,0.27164,0.00228668,0.0005863222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006150967,0.0001174514,0.9916005,0.00002576253,0.0002864324,0.0009133475,0.000009437105,0.0004375599,0.0004585033],"genre_scores_gemma":[0.2205124,0.00003589063,0.7785872,0.00003340727,0.00009032692,0.0006819066,0.00001234998,0.00003248522,0.00001398741],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6595301,"threshold_uncertainty_score":0.6711574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05938999270772182,"score_gpt":0.2740241178548742,"score_spread":0.2146341251471524,"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."}}