{"id":"W2057500293","doi":"10.1364/ao.43.000403","title":"Target detection and recognition improvements by use of spatiotemporal fusion","year":2004,"lang":"en","type":"article","venue":"Applied Optics","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Clutter; Artificial intelligence; Thresholding; Computer science; Pixel; Noise (video); Computer vision; Constant false alarm rate; Gaussian noise; Sensor fusion; Pattern recognition (psychology); Object detection; False alarm; Radar; Image (mathematics); Telecommunications","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":[0.0008869675,0.0004414633,0.0004814329,0.0006012463,0.0001511236,0.0004854026,0.0005274631,0.0004482329,0.0006803867],"category_scores_gemma":[0.002850437,0.0002510762,0.0005064783,0.0005697759,0.0002471117,0.001458276,0.0007899593,0.0005189572,0.0003483843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003086948,"about_ca_system_score_gemma":0.0002751592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007459934,"about_ca_topic_score_gemma":0.0008043345,"domain_scores_codex":[0.9992925,0.00009335772,0.00006235938,0.0001544287,0.000337791,0.0000595345],"domain_scores_gemma":[0.9989774,0.0002844399,0.0001244521,0.0001770381,0.0004100866,0.00002650113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002443348,0.00012069,0.001870163,0.0001108734,0.00005192668,0.0001096269,0.0001133929,0.05532216,0.4981911,0.003070399,0.0005931166,0.4402022],"study_design_scores_gemma":[0.00002282913,0.0002704689,0.003126882,0.000009605453,0.00006326607,0.0003997936,0.00003452481,0.7315376,0.2592801,0.001787031,0.003426772,0.00004114463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09411217,0.0005495328,0.9028788,0.0001259577,0.0000575326,0.00003747951,0.00005612092,0.0007837111,0.001398661],"genre_scores_gemma":[0.5723929,0.0005063913,0.4254544,0.00009949868,0.00006377201,0.00004699851,0.000157996,0.0000640486,0.001213999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008869675,"threshold_uncertainty_score":0.004690766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02993193696345349,"score_gpt":0.2221861161029369,"score_spread":0.1922541791394834,"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."}}