{"id":"W1989812546","doi":"10.1007/s00138-011-0358-4","title":"Camera–projector matching using unstructured video","year":2011,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer vision; Artificial intelligence; Scale-invariant feature transform; Computer science; Projector; Camera resectioning; Image warping; Structured light; Camera auto-calibration; Homography; Epipolar geometry; Computer graphics (images); Triangulation; Feature extraction; Mathematics; Image (mathematics)","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":[0.0007885142,0.001361159,0.001413928,0.003086929,0.0006748407,0.001630188,0.001332176,0.001641931,0.004356369],"category_scores_gemma":[0.003556826,0.001120982,0.001128477,0.002787237,0.0005294361,0.002035364,0.002400582,0.001337288,0.002125433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005042408,"about_ca_system_score_gemma":0.001207976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003584818,"about_ca_topic_score_gemma":0.004659427,"domain_scores_codex":[0.9985934,0.0002141452,0.00006002411,0.000337517,0.0006395719,0.0001552868],"domain_scores_gemma":[0.9989327,0.0002155411,0.0001042521,0.0003224037,0.0003469849,0.00007819354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000742917,0.0002101297,0.00157216,0.0002183279,0.0002062742,0.0003936901,0.0001379124,0.05281653,0.1624798,0.007175379,0.006113345,0.7679335],"study_design_scores_gemma":[0.0000665827,0.0001525729,0.001886702,0.00002204599,0.00005642067,0.0006868477,0.00008765663,0.9066664,0.07656837,0.008611116,0.005152355,0.00004295587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01368206,0.00009212451,0.983595,0.0000561136,0.00004584395,0.00008084975,0.0001051769,0.001030941,0.001311974],"genre_scores_gemma":[0.1516692,0.0002037449,0.8431565,0.00008102719,0.00006023032,0.00007799334,0.0007641275,0.0003945097,0.003592708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004356369,"threshold_uncertainty_score":0.01457345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02268972531072795,"score_gpt":0.3043570266384226,"score_spread":0.2816673013276947,"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."}}