{"id":"W1976435971","doi":"10.1117/12.503167","title":"<title>Object extraction in video sequences based on the spatio-temporal independent component analysis</title>","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Independent component analysis; Video tracking; Computer vision; Object (grammar); Video compression picture types; Data compression; Component (thermodynamics); Image segmentation; Feature extraction; Pattern recognition (psychology)","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":[0.0002760487,0.0005358931,0.0005194527,0.001385492,0.0003285971,0.0008156512,0.000424141,0.000533003,0.01954423],"category_scores_gemma":[0.0009442964,0.0001403722,0.0002692917,0.001456316,0.0005160361,0.000829575,0.0002646256,0.0005132398,0.01191445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003874176,"about_ca_system_score_gemma":0.0004759294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002160919,"about_ca_topic_score_gemma":0.003116135,"domain_scores_codex":[0.9998131,0.00002426538,0.0000181243,0.00003515992,0.00009718784,0.00001219652],"domain_scores_gemma":[0.9993406,0.0001333457,0.00005009797,0.00008083726,0.0003565981,0.0000384565],"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":[0.0003334664,0.00008386282,0.0005631581,0.0004435112,0.00002728812,0.0003916966,0.00008450332,0.003322571,0.1755991,0.01511786,0.06957021,0.7344627],"study_design_scores_gemma":[0.00007107727,0.0006351611,0.008386314,0.0002534409,0.00009277227,0.001770051,0.0001298701,0.1734616,0.4179279,0.01268476,0.3844625,0.0001246048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03482808,0.009523148,0.8748898,0.002530338,0.005987564,0.000536179,0.00157431,0.005524491,0.06460603],"genre_scores_gemma":[0.1991254,0.0150685,0.5443628,0.0008680067,0.00374639,0.0003768937,0.006375489,0.001452184,0.2286244],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01954423,"threshold_uncertainty_score":0.06538188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01480231341588428,"score_gpt":0.2476960885546121,"score_spread":0.2328937751387278,"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."}}