{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001366405,0.0002785652,0.0002463341,0.0003580489,0.000166582,0.0001301867,0.00007388694,0.0002495131,0.0001895204],"category_scores_gemma":[0.000002716807,0.0003314673,0.000149853,0.0006685241,0.00001992909,0.001023977,1.261222e-7,0.0003196108,0.0007539514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002083094,"about_ca_system_score_gemma":0.00002930631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006531398,"about_ca_topic_score_gemma":0.0002300811,"domain_scores_codex":[0.9982136,0.00006425666,0.0008156885,0.0002331122,0.0003943706,0.000278895],"domain_scores_gemma":[0.9990977,0.00006940849,0.0001035743,0.0002464023,0.0003521051,0.0001308029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005342755,0.00006308693,0.00001204652,0.0002422209,0.0001229074,0.00001213528,0.001060732,0.9126932,0.01484958,0.00003153457,0.00002456188,0.07083455],"study_design_scores_gemma":[0.0005609504,0.00006893308,0.0003727363,0.0001578898,0.0001254047,0.00008353918,0.0007716744,0.3879101,0.6049426,0.0000326261,0.004495536,0.0004780731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1003401,0.00007406404,0.8947936,0.000018237,0.002951557,0.000418376,0.0001528919,0.0008027379,0.0004484721],"genre_scores_gemma":[0.9985039,0.0004235198,0.0003259014,0.00004749993,0.00006704027,0.0001707645,0.0003060339,0.00005323664,0.0001021464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8981638,"threshold_uncertainty_score":0.9999138,"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."}}