{"id":"W2605547838","doi":"10.1007/s11548-017-1590-9","title":"Hand-eye calibration for surgical cameras: a Procrustean Perspective-n-Point solution","year":2017,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Robarts Clinical Trials; Western University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation","keywords":"Computer vision; Artificial intelligence; Stylus; Computer science; Perspective (graphical); Metric (unit); Calibration; Point (geometry); Camera resectioning; Eye tracking; Mathematics; Engineering","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":[0.0009513314,0.0001285751,0.0003271576,0.0002384683,0.0002572158,0.0004924128,0.0007302992,0.000109662,0.000006637913],"category_scores_gemma":[0.0002561765,0.000102357,0.0002064019,0.00002969552,0.0002117563,0.001053946,0.0001281531,0.0001874178,7.21681e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009122411,"about_ca_system_score_gemma":0.0001153178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001086419,"about_ca_topic_score_gemma":0.000003372786,"domain_scores_codex":[0.9987615,0.0001228449,0.0004794146,0.0002159509,0.0002459,0.0001744058],"domain_scores_gemma":[0.9979025,0.0003719262,0.0005438349,0.0001899686,0.0009013297,0.00009051527],"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":[0.002819977,0.001257142,0.07950287,0.00008562474,0.002433095,0.001143636,0.002434203,0.00007348943,0.01027947,0.3506265,0.0473088,0.5020352],"study_design_scores_gemma":[0.010639,0.004394816,0.3174315,0.001690133,0.000223778,0.01438337,0.0002183921,0.5045754,0.02184937,0.09292527,0.02955866,0.002110373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04524602,0.0002425893,0.9374624,0.0142117,0.00231321,0.000118199,0.000002559518,0.00003559436,0.0003677452],"genre_scores_gemma":[0.97936,0.00007645789,0.01947196,0.0002304481,0.0008199873,0.000007892468,0.000002509791,0.000005453999,0.0000253292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9341139,"threshold_uncertainty_score":0.4748348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04744347623453048,"score_gpt":0.3207285916077533,"score_spread":0.2732851153732228,"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."}}