{"id":"W4206679962","doi":"10.1109/tits.2021.3135015","title":"License Plate Detection via Information Maximization","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; Korea Creative Content Agency; National Research Foundation of Korea; Ministry of Culture, Sports and Tourism; National Natural Science Foundation of China; National Research Foundation","keywords":"Computer science; Artificial intelligence; Object detection; Detector; Minimum bounding box; Bounding overwatch; Complement (music); Maximization; Variety (cybernetics); License; Object (grammar); Pattern recognition (psychology); Encoder; Computer vision; State (computer science); Image (mathematics); Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001170367,0.001685513,0.001634048,0.001349746,0.0004668783,0.001157727,0.003294387,0.001498544,0.003324579],"category_scores_gemma":[0.00346288,0.000827592,0.00118312,0.001033154,0.001265977,0.001674662,0.001829505,0.002046659,0.003274599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001215694,"about_ca_system_score_gemma":0.001744577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007532672,"about_ca_topic_score_gemma":0.008229638,"domain_scores_codex":[0.998909,0.0002245259,0.00004431025,0.0004379483,0.0002351416,0.0001489965],"domain_scores_gemma":[0.998908,0.0004777172,0.0001312108,0.0001601225,0.0002583501,0.00006449083],"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":[0.0003068186,0.0002189048,0.001997471,0.000216598,0.0001379506,0.0002055663,0.00009120238,0.5396488,0.009160083,0.007633206,0.01549811,0.4248853],"study_design_scores_gemma":[0.000006381958,0.00001564198,0.0002240684,0.000006794512,0.000008881216,0.00002317304,0.000008062089,0.9943101,0.00209927,0.002735002,0.0005543848,0.000008130818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01953183,0.0005318553,0.9716634,0.0004439594,0.00005975805,0.000109216,0.0006021824,0.00381657,0.003241229],"genre_scores_gemma":[0.5998982,0.0006615738,0.3750281,0.000815686,0.0002781991,0.0003240292,0.005171965,0.0006550016,0.01716723],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007532672,"threshold_uncertainty_score":0.01497769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01177584711699145,"score_gpt":0.1972820670558435,"score_spread":0.1855062199388521,"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."}}