{"id":"W1590852305","doi":"10.11575/prism/31371","title":"THE EFFECTS OF CAPTURE CONDITIONS ON THE CAMSHIFT FACE TRACKER","year":2001,"lang":"en","type":"article","venue":"PRISM (University of Calgary)","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Facial motion capture; Computer vision; Computer science; Artificial intelligence; Tracking (education); Face (sociological concept); Orientation (vector space); Facial recognition system; Face detection; Computer graphics (images); Feature extraction; 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":[0.002978711,0.0008037634,0.001003962,0.0008409162,0.001623116,0.001940306,0.0008278969,0.001723231,0.005216863],"category_scores_gemma":[0.04367021,0.0008635144,0.0005295302,0.0008028483,0.0009321648,0.001777347,0.002110097,0.00130052,0.001282553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00159141,"about_ca_system_score_gemma":0.0008447816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007142194,"about_ca_topic_score_gemma":0.008145998,"domain_scores_codex":[0.9970561,0.000565146,0.0002686504,0.0006175133,0.001110582,0.0003819963],"domain_scores_gemma":[0.9735736,0.02078832,0.0009867406,0.001367725,0.002759439,0.0005241677],"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":[0.0101682,0.0005386816,0.03234908,0.001563964,0.0002994163,0.002255237,0.002975633,0.02725118,0.7285212,0.003067924,0.00595174,0.1850577],"study_design_scores_gemma":[0.0004722292,0.004820757,0.2586964,0.0003420887,0.001036641,0.008189126,0.0016596,0.1059897,0.5951998,0.00430826,0.01886121,0.0004242128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7978966,0.005036646,0.1693684,0.001639199,0.00200192,0.0007423972,0.001215995,0.001482036,0.02061695],"genre_scores_gemma":[0.9536989,0.00187597,0.0325701,0.001192541,0.0002719972,0.0004025823,0.001476421,0.0007855689,0.007725917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007142194,"threshold_uncertainty_score":0.01745218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00836609999482293,"score_gpt":0.207651321543255,"score_spread":0.1992852215484321,"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."}}