{"id":"W1973912788","doi":"10.1117/12.594572","title":"A simple and fast text localization algorithm for indoor mobile robot navigation","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Mobile robot; Artificial intelligence; Computer vision; Robot; Mobile robot navigation; Focus (optics); Feature (linguistics); Variance (accounting); Algorithm; Robot control","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":[0.0002034972,0.0007724057,0.0006360151,0.00102798,0.0005282225,0.00037617,0.0008328208,0.0007451724,0.003269099],"category_scores_gemma":[0.0006477355,0.0002719544,0.0004302301,0.0006851951,0.000232577,0.0007388213,0.0004007713,0.0004543887,0.002088907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000351136,"about_ca_system_score_gemma":0.0005625983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002034742,"about_ca_topic_score_gemma":0.002941888,"domain_scores_codex":[0.9997754,0.00001955646,0.00001206135,0.00007536457,0.0001003769,0.00001732461],"domain_scores_gemma":[0.9997569,0.0000404339,0.00003039131,0.00002290814,0.0001315978,0.0000178576],"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":[0.0003160485,0.00006296639,0.0006186747,0.0001653766,0.00004075326,0.0003081427,0.00007919462,0.011449,0.1709809,0.00182834,0.009711297,0.8044392],"study_design_scores_gemma":[0.0002389851,0.0006622446,0.004764286,0.0000380292,0.0001318346,0.001576815,0.0001232522,0.7466008,0.2027193,0.002792063,0.0401908,0.0001615739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01519347,0.0003591516,0.9776297,0.000101739,0.000144029,0.0000828933,0.0001204617,0.005216583,0.001151989],"genre_scores_gemma":[0.1271764,0.0002164992,0.865939,0.00008745892,0.00007165711,0.0001514879,0.0004926333,0.0001685968,0.005696306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003269099,"threshold_uncertainty_score":0.01093626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009046320990248094,"score_gpt":0.2527868024053935,"score_spread":0.2437404814151454,"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."}}