{"id":"W2442176446","doi":"10.5194/isprs-archives-xli-b5-757-2016","title":"Toward an Automatic Calibration of Dual Fluoroscopy Imaging Systems","year":2016,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Computer vision; Artificial intelligence; Computer science; Calibration; Bundle adjustment; Pixel; Translation (biology); Rotation (mathematics); Filter (signal processing); Mathematics; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00108367,0.0003813385,0.0004244621,0.0008366464,0.0006704631,0.0005791293,0.00108062,0.00008367185,0.000007144713],"category_scores_gemma":[0.0004403989,0.0002246377,0.0002589404,0.0007161711,0.002072094,0.0008468927,0.0003894851,0.0002427674,0.00000166958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005520547,"about_ca_system_score_gemma":0.000164465,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5011449,"about_ca_topic_score_gemma":0.04254974,"domain_scores_codex":[0.9958467,0.0002673126,0.001556792,0.0003088143,0.001583261,0.0004371522],"domain_scores_gemma":[0.9971516,0.0007730451,0.001184646,0.0004460058,0.0002951501,0.0001495462],"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":[0.0000579497,0.0000147728,0.0002316448,0.00008113528,0.00006807598,2.672549e-7,0.002327094,0.03596376,0.0108177,0.00002292213,0.00003157655,0.9503831],"study_design_scores_gemma":[0.0006582972,0.0001083857,0.001328732,0.0005248484,0.00004089271,0.00009581915,0.001455575,0.9749151,0.01721113,0.002741151,0.000642481,0.0002775267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01908991,0.00003805535,0.97375,0.001616419,0.002106386,0.0005520002,0.0001179253,0.00008280489,0.002646509],"genre_scores_gemma":[0.9965626,0.0001505862,0.002779099,0.0002909236,0.0001302114,3.958507e-7,0.00003782349,0.00001627135,0.00003209498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9774727,"threshold_uncertainty_score":0.9749212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01608945761438915,"score_gpt":0.2402493088713342,"score_spread":0.224159851256945,"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."}}