{"id":"W2626992568","doi":"10.1109/tip.2017.2716194","title":"A Closed-Form Solution to Single Underwater Camera Calibration Using Triple Wavelength Dispersion and Its Application to Single Camera 3D Reconstruction","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; University of Alberta; Alberta Innovates - Technology Futures","keywords":"Robustness (evolution); Computer science; Wavelength; Correctness; Camera resectioning; Artificial intelligence; Computer vision; Dispersion (optics); Calibration; Ground truth; Underwater; Optics; Algorithm; Mathematics; Physics","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":[0.0002278436,0.0002228847,0.0001943351,0.0002716078,0.001183227,0.001035594,0.0003534471,0.0001084924,0.000008909204],"category_scores_gemma":[0.00002441312,0.0002166179,0.00005009814,0.0002373308,0.00006687024,0.003162159,0.0000152433,0.0001929967,0.00001563973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003097283,"about_ca_system_score_gemma":0.00005905522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009342947,"about_ca_topic_score_gemma":0.00006289062,"domain_scores_codex":[0.9984306,0.0000390123,0.0003252246,0.0005763011,0.0002978188,0.0003310957],"domain_scores_gemma":[0.9990026,0.00001531769,0.000164493,0.0003948181,0.000247422,0.0001753177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003014592,0.00009159773,0.000006012505,0.00002731995,0.000004356825,4.053301e-7,0.0004237753,0.00006075395,0.5903188,0.0000216718,0.00000558697,0.4090095],"study_design_scores_gemma":[0.0002028042,0.0003025876,0.00005516407,0.0002248959,0.00002061,0.0000146367,0.0000505295,0.3539415,0.644687,0.000211654,0.00004118306,0.0002474797],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1075084,0.00001318183,0.8897652,0.001239716,0.0002640201,0.0005437147,0.000004325883,0.0002318065,0.0004296423],"genre_scores_gemma":[0.8968264,0.000006906807,0.1027986,0.0001672169,0.00005797834,0.00005936325,0.000001288637,0.00001950996,0.00006269095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.789318,"threshold_uncertainty_score":0.9986255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04672497867736423,"score_gpt":0.2844082301916386,"score_spread":0.2376832515142744,"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."}}