{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001666869,0.0009857345,0.0006428273,0.0006628231,0.0003700584,0.0006978522,0.0008713055,0.000815744,0.002453584],"category_scores_gemma":[0.002436137,0.0003933983,0.0006069087,0.0009241068,0.0005023901,0.0007145059,0.0005026661,0.001043171,0.0009278061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007528006,"about_ca_system_score_gemma":0.001228838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001749683,"about_ca_topic_score_gemma":0.001668619,"domain_scores_codex":[0.9988871,0.00029682,0.00004048975,0.0001537966,0.0005660267,0.00005582787],"domain_scores_gemma":[0.9990284,0.0002952771,0.0001072795,0.0000768396,0.0004680527,0.00002413757],"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":[0.0001771704,0.0001291919,0.0005704128,0.0003770988,0.00009557546,0.00009705492,0.0001018458,0.3491118,0.07550583,0.04513376,0.005876176,0.5228242],"study_design_scores_gemma":[0.0000208652,0.0001687899,0.00027598,0.00001475447,0.00002128389,0.0001111479,0.000009374272,0.9703373,0.01524193,0.004826327,0.008948971,0.00002322298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005182534,0.00006297798,0.9989412,0.00002447543,0.00001139193,0.00001307932,0.000006272566,0.00006824938,0.0003540139],"genre_scores_gemma":[0.07490479,0.0003810058,0.9208647,0.00009937974,0.00007833407,0.0002216577,0.0001087003,0.00009290841,0.003248454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002453584,"threshold_uncertainty_score":0.008815348,"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."}}