{"id":"W2164416728","doi":"10.1109/imtc.2007.379068","title":"Real-time Vision-based Hand Gesture Recognition Using Haar-like Features","year":2007,"lang":"en","type":"article","venue":"Conference proceedings - IEEE Instrumentation/Measurement Technology Conference","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":302,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Gesture recognition; Gesture; Artificial intelligence; Parsing; Haar-like features; Computer vision; AdaBoost; Set (abstract data type); Speech recognition; Context (archaeology); Pattern recognition (psychology); Face detection; Facial recognition system; Classifier (UML)","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002021122,0.0007435524,0.0006930435,0.001471437,0.0008401495,0.001264561,0.00167318,0.0008218393,0.0001191892],"category_scores_gemma":[0.0002728933,0.0007447794,0.0001486364,0.001983323,0.0006015128,0.001684952,0.0002019724,0.0007971163,0.000239834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006101539,"about_ca_system_score_gemma":0.0009871635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006177599,"about_ca_topic_score_gemma":0.0000903076,"domain_scores_codex":[0.9944597,0.00006903381,0.001151585,0.0015141,0.001699831,0.001105739],"domain_scores_gemma":[0.9934785,0.00009297816,0.0009382058,0.0006017489,0.004553354,0.0003352261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000100441,0.000307451,0.005096212,0.0001866019,0.0001334042,0.00003052859,0.001300196,0.00001015245,0.8336304,0.003536062,0.0009202765,0.1547482],"study_design_scores_gemma":[0.005477442,0.001234011,0.009795824,0.002921354,0.000229812,0.0004641846,0.002803876,0.03628883,0.9169238,0.01995743,0.001082551,0.002820856],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6288651,0.00008186659,0.3590203,0.002393903,0.001342972,0.001652801,0.0000224203,0.001687151,0.004933523],"genre_scores_gemma":[0.957065,0.00004617631,0.04209203,0.0003024619,0.0001381938,0.0001520857,0.0000299851,0.00004566397,0.0001284273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3281999,"threshold_uncertainty_score":0.9997723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05106941278014394,"score_gpt":0.2818192476971611,"score_spread":0.2307498349170172,"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."}}