{"id":"W2123977633","doi":"10.1109/tpami.2008.202","title":"Calibration of Cameras with Radially Symmetric Distortion","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Catadioptric system; Distortion (music); Artificial intelligence; Computer vision; Calibration; Camera resectioning; Conic section; Pixel; Projection (relational algebra); Homography; Computer science; Mathematics; Computer graphics (images); Optics; Physics; Geometry; Algorithm","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":[0.001009587,0.001576586,0.001321302,0.001253876,0.000582705,0.001248932,0.001951138,0.001096873,0.002805292],"category_scores_gemma":[0.004074092,0.0008890257,0.0007487008,0.002029695,0.0006641279,0.001728187,0.002376169,0.001591406,0.001979197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007411343,"about_ca_system_score_gemma":0.0009072722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002457624,"about_ca_topic_score_gemma":0.002437267,"domain_scores_codex":[0.9982485,0.0003068207,0.00009272576,0.0004875473,0.0007634036,0.0001011681],"domain_scores_gemma":[0.9984738,0.0002503024,0.0002632522,0.0005497836,0.0004171264,0.00004569689],"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":[0.0001970678,0.0000550087,0.00157872,0.0002018568,0.0001087097,0.0001500683,0.0002357859,0.1831908,0.02412388,0.02739259,0.003544,0.7592216],"study_design_scores_gemma":[0.00004343144,0.0001030494,0.001260058,0.00005576393,0.00003048987,0.0007109881,0.00009101845,0.9304798,0.03431762,0.02238529,0.01044826,0.00007436109],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002471204,0.000097952,0.9966235,0.00001309619,0.000008531106,0.00002165899,0.00002329861,0.0002962086,0.0004445189],"genre_scores_gemma":[0.07624466,0.0003035754,0.9219724,0.00003386743,0.00002324611,0.00007152361,0.0002034885,0.000106308,0.001041094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002805292,"threshold_uncertainty_score":0.009384632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03083529317310139,"score_gpt":0.2527808609770568,"score_spread":0.2219455678039554,"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."}}