{"id":"W2171037494","doi":"10.1109/mlsp.2007.4414299","title":"Autonomous Stereo Camera Parameter Estimation for Outdoor Visual Servoing","year":2007,"lang":"en","type":"article","venue":"Machine learning for signal processing ...","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Motion Metrics International (Canada); University of British Columbia","funders":"","keywords":"Computer vision; Artificial intelligence; Stereo camera; Visual servoing; Computer stereo vision; Camera auto-calibration; Computer science; Camera resectioning; Stereo cameras; Triangulation; Calibration; Epipolar geometry; Process (computing); Stereopsis; Robot; Stereo imaging; Camera matrix; Robot calibration; Pinhole camera model; Workspace; Robot kinematics; Mobile robot; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000956249,0.0002339602,0.0002437693,0.0001931506,0.0006951517,0.0004445766,0.0003910306,0.0000669092,0.00000353255],"category_scores_gemma":[0.0002647952,0.0002195313,0.0001172034,0.0002719582,0.00003835049,0.0009578734,0.000106402,0.0002975999,0.000007878813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007651518,"about_ca_system_score_gemma":0.00007644117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001241657,"about_ca_topic_score_gemma":0.000003600877,"domain_scores_codex":[0.9982496,0.00003382947,0.0004127985,0.0005206047,0.0002290769,0.000554124],"domain_scores_gemma":[0.9987886,0.0004772991,0.000292994,0.0001466367,0.0001745912,0.0001199131],"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":[0.0000572023,0.00004218167,0.0006077936,0.0001340688,0.000008517164,0.000002474193,0.0005759979,0.01774617,0.001900951,0.0004101111,0.00002258958,0.978492],"study_design_scores_gemma":[0.0007564535,0.0003007647,0.0001994745,0.0001041848,0.00001277927,0.00001733119,0.00005180173,0.9858811,0.00314554,0.003063287,0.006171267,0.0002959562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004803838,0.0002423106,0.9932366,0.0006019698,0.0001500855,0.0003911355,0.000001493862,0.0004497365,0.0001228326],"genre_scores_gemma":[0.530454,4.195033e-7,0.4686317,0.0003814679,0.00007715185,0.00002356935,0.00001679721,0.00002651467,0.0003883487],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.978196,"threshold_uncertainty_score":0.8952225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01734549365629296,"score_gpt":0.3257770732381099,"score_spread":0.308431579581817,"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."}}