{"id":"W2154215478","doi":"10.1109/icsmc.2004.1400797","title":"Calibration of outdoor cameras from cast shadows","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alpha Technologies (Canada)","funders":"","keywords":"Computer vision; Artificial intelligence; Calibration; Computer science; Camera resectioning; Computer graphics (images); Field of view; Camera auto-calibration; Lens (geology); Orientation (vector space); Mathematics; Optics; Physics; Geometry","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":[0.0002883674,0.00072912,0.0007065736,0.001138183,0.0005337188,0.0008111966,0.0007644198,0.0004658232,0.002485753],"category_scores_gemma":[0.00177487,0.0006924265,0.0004311981,0.001034874,0.0004680318,0.0007178913,0.001025911,0.0008638942,0.0009309864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003930501,"about_ca_system_score_gemma":0.0004244053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00246309,"about_ca_topic_score_gemma":0.004562437,"domain_scores_codex":[0.9994073,0.0000848995,0.0000136585,0.0001549179,0.0002861276,0.00005298581],"domain_scores_gemma":[0.9995746,0.00007544867,0.00007032535,0.0001228203,0.0001312081,0.00002568246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001447549,0.0000693169,0.003291658,0.0001846129,0.00009266174,0.0002210838,0.0003549464,0.12148,0.09740394,0.0124254,0.004610298,0.7597213],"study_design_scores_gemma":[0.00004770411,0.0001280176,0.008935988,0.00006431705,0.00004338864,0.0009801415,0.0001840636,0.8311872,0.1190105,0.01559374,0.0237554,0.0000695463],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01787528,0.0002543868,0.9786913,0.00002557699,0.00002452285,0.0000192101,0.00006179891,0.0009210961,0.002126777],"genre_scores_gemma":[0.3995976,0.0006060009,0.5963006,0.00005184969,0.00007482694,0.00005190758,0.000545458,0.0002998294,0.002471896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002485753,"threshold_uncertainty_score":0.008315682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00927818829897817,"score_gpt":0.1915668512099121,"score_spread":0.182288662910934,"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."}}