{"id":"W1580500789","doi":"10.1109/icsmc.2002.1173369","title":"Camera self-calibration from two views","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Skew; Principal (computer security); Point (geometry); Computer science; Scale (ratio); Vanishing point; Computer vision; Calibration; Artificial intelligence; Horizontal and vertical; Image (mathematics); Algorithm; Mathematics; Geometry; Geography; Statistics","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":[0.000578814,0.0009328919,0.001000693,0.0007764269,0.0004503778,0.001708486,0.0008820305,0.001291602,0.005189526],"category_scores_gemma":[0.002625646,0.001045487,0.0009242957,0.00106195,0.0006527911,0.002150713,0.002919343,0.002763801,0.002805866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005233472,"about_ca_system_score_gemma":0.0008597461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001776348,"about_ca_topic_score_gemma":0.001989484,"domain_scores_codex":[0.998434,0.00022084,0.00005176429,0.0004847102,0.0007288787,0.00007981691],"domain_scores_gemma":[0.9993457,0.0001087778,0.00005475293,0.0002560712,0.0002017134,0.00003295159],"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":[0.000274016,0.00007050902,0.001512267,0.0006200795,0.0001912407,0.0005334585,0.0006987664,0.1074789,0.1200702,0.06548309,0.008753542,0.6943139],"study_design_scores_gemma":[0.00008596826,0.000166361,0.005363701,0.0002143987,0.000111976,0.002270182,0.0003001354,0.7311795,0.1255333,0.05256164,0.08195738,0.0002553345],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006752775,0.0002835915,0.9873338,0.0001397276,0.0001233121,0.00003388112,0.00008303564,0.0005648384,0.00468512],"genre_scores_gemma":[0.2915995,0.001339682,0.6931063,0.0002985105,0.0001195288,0.0001200875,0.0006273375,0.0005080331,0.01228101],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005189526,"threshold_uncertainty_score":0.01736075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02156709436712926,"score_gpt":0.2925924502891693,"score_spread":0.27102535592204,"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."}}