{"id":"W2147329398","doi":"10.1109/icpr.2008.4761312","title":"Trajectories normalization for viewpoint invariant gait recognition","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Université Laval","funders":"","keywords":"Homography; Normalization (sociology); Artificial intelligence; Invariant (physics); Computer science; Computer vision; Gait; Gait cycle; Mathematics; Kinematics; Statistics; Physics; Physical medicine and rehabilitation","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":[0.0002606458,0.0006645122,0.0005621778,0.001100514,0.000273842,0.0004720962,0.0005978266,0.0002998621,0.003383645],"category_scores_gemma":[0.001093853,0.0002907804,0.0004515055,0.001151406,0.0002903072,0.000603044,0.000452204,0.0005010439,0.002141387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003349737,"about_ca_system_score_gemma":0.0005198603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002726245,"about_ca_topic_score_gemma":0.003703815,"domain_scores_codex":[0.9996033,0.0000478189,0.0000205102,0.0001319601,0.0001621842,0.00003419816],"domain_scores_gemma":[0.9996476,0.00005704125,0.00004668554,0.00009094336,0.0001343298,0.00002339085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002079269,0.00004424706,0.001075086,0.0001016988,0.00004970818,0.0001389192,0.00007317019,0.01487367,0.1030541,0.003538526,0.003224785,0.8736181],"study_design_scores_gemma":[0.00003262584,0.000244826,0.01000299,0.00006384285,0.00006607619,0.001178729,0.0001753228,0.7820207,0.1756772,0.008168725,0.02228785,0.00008107058],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01622288,0.0003355774,0.9793488,0.00003522243,0.00009277886,0.00005473138,0.0003096952,0.002150998,0.001449298],"genre_scores_gemma":[0.2454796,0.0007882611,0.7448495,0.0000659159,0.00007912111,0.0001543112,0.002393578,0.0006111106,0.005578672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003383645,"threshold_uncertainty_score":0.0113194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09201979432990662,"score_gpt":0.2749756539555689,"score_spread":0.1829558596256623,"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."}}