{"id":"W2160583984","doi":"10.1109/tcsvt.2009.2031380","title":"Perspective 3-D Euclidean Reconstruction With Varying Camera Parameters","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Euclidean geometry; Computer vision; Perspective (graphical); Artificial intelligence; Camera matrix; Reprojection error; Computer science; Metric (unit); Camera resectioning; Euclidean distance; Focus (optics); Structure from motion; Projective test; Matrix decomposition; Iterative reconstruction; Factorization; Algorithm; Mathematics; Camera auto-calibration; Motion (physics); Image (mathematics); Pinhole camera model","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":[0.0006288076,0.00105995,0.0006140378,0.0005256116,0.0002435536,0.0005980463,0.000687475,0.0008611386,0.001317628],"category_scores_gemma":[0.002404027,0.000541367,0.0006831954,0.0008823785,0.0004681,0.001730003,0.0008118362,0.001016291,0.0006404415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004298251,"about_ca_system_score_gemma":0.0004433327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001496713,"about_ca_topic_score_gemma":0.001519071,"domain_scores_codex":[0.9994586,0.0001261465,0.00002439055,0.0001325092,0.0002239263,0.00003442572],"domain_scores_gemma":[0.9994416,0.0001749356,0.00009166231,0.0001537169,0.0001115251,0.00002657364],"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":[0.0004390587,0.00008817358,0.001310191,0.000267096,0.0001204631,0.0003654911,0.0003284001,0.5149923,0.1186566,0.01570286,0.002102303,0.3456271],"study_design_scores_gemma":[0.00001504896,0.00008303099,0.0004833167,0.000009637573,0.00001439211,0.0002882676,0.00003218649,0.9612522,0.03358837,0.002566156,0.00163486,0.00003253944],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01610349,0.000117902,0.9828447,0.00004709573,0.00002028831,0.00002004072,0.00004272799,0.0002748104,0.0005289302],"genre_scores_gemma":[0.2050302,0.0004673338,0.7929245,0.00005307515,0.00003929972,0.000041964,0.0002372114,0.00009479062,0.001111669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001496713,"threshold_uncertainty_score":0.004407883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01883296126281339,"score_gpt":0.2609380453044528,"score_spread":0.2421050840416394,"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."}}