{"id":"W2081367248","doi":"10.1109/wacv.2015.122","title":"A General Framework for Fast 3D Object Detection and Localization Using an Uncalibrated Camera","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Computer vision; Memory footprint; Pattern recognition (psychology); Object detection; Feature matching; Feature extraction; Search engine indexing; Cognitive neuroscience of visual object recognition; Matching (statistics); Feature (linguistics); 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":[0.00007796648,0.000110532,0.0001049507,0.00007289065,0.00006555858,0.00008440251,0.00002831447,0.0001264978,0.000006979516],"category_scores_gemma":[0.00003502384,0.0001088641,0.00001564438,0.0001980711,0.00001564113,0.0001899285,0.000006098477,0.0000542632,0.000001227026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005777805,"about_ca_system_score_gemma":0.00001874938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001027635,"about_ca_topic_score_gemma":0.00006823529,"domain_scores_codex":[0.999454,0.00002339428,0.0001485218,0.0001389408,0.00008675156,0.0001484212],"domain_scores_gemma":[0.999661,0.0000169992,0.00002000937,0.0001005092,0.00009100452,0.0001105177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000117936,0.000009813842,0.000335346,0.0000192644,0.000009965019,4.388963e-7,0.0001645573,0.9919089,0.003492315,0.0009889437,0.00003064868,0.003028001],"study_design_scores_gemma":[0.0002737961,0.00007612803,0.00005384073,0.00001159493,0.00001710725,0.000004187308,0.0001108248,0.9891602,0.008733446,0.001211503,0.0001970681,0.0001503027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1845111,0.00003655372,0.8148057,0.000004964465,0.0001895848,0.0001524057,0.000003087918,0.0002052491,0.00009135398],"genre_scores_gemma":[0.9251587,0.000007437099,0.07447278,0.00008821335,0.0001623892,0.000006171804,0.00004225097,0.00004111031,0.00002090027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7406477,"threshold_uncertainty_score":0.4439349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03122097822629254,"score_gpt":0.2519205511609703,"score_spread":0.2206995729346778,"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."}}