{"id":"W4249596020","doi":"10.32920/ryerson.14663148","title":"Object extraction in video sequences based on spatiotemporal independent component analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Video tracking; Segmentation; Video processing; Motion compensation; Pattern recognition (psychology); Video post-processing; Object (grammar); Independent component analysis; Block-matching algorithm; Wavelet; Video compression picture types","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":[0.0002556991,0.0006189163,0.0004524036,0.001453236,0.0002442987,0.0004521212,0.0003270139,0.0003701652,0.0006406005],"category_scores_gemma":[0.000965089,0.0002077161,0.0005541019,0.001461269,0.0003301704,0.0007873317,0.0003318691,0.0004691023,0.0004026954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002322768,"about_ca_system_score_gemma":0.0005407901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001845695,"about_ca_topic_score_gemma":0.00199844,"domain_scores_codex":[0.9997616,0.0000289688,0.00001871665,0.00004660821,0.0001258155,0.00001825179],"domain_scores_gemma":[0.9997291,0.00006803127,0.00004479455,0.00002932802,0.0001160505,0.00001269589],"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":[0.0002510986,0.00007589204,0.001159203,0.0002415935,0.00008908664,0.0003019737,0.0001204776,0.03884032,0.3748247,0.006845009,0.001402532,0.575848],"study_design_scores_gemma":[0.0000155634,0.0001313575,0.004005156,0.00002351158,0.00007537638,0.0004199939,0.000042853,0.8530959,0.1351101,0.002933122,0.00411415,0.0000329563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01827017,0.0003257579,0.9802571,0.00005326256,0.0000485692,0.00005018738,0.00005815168,0.0003279301,0.0006088693],"genre_scores_gemma":[0.1986301,0.001281966,0.7972957,0.00005098012,0.0001027813,0.0001056429,0.0004535574,0.00007143068,0.002007733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001845695,"threshold_uncertainty_score":0.003669977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03063036966420425,"score_gpt":0.3133088526688914,"score_spread":0.2826784830046871,"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."}}