{"id":"W1979954176","doi":"10.1109/tmm.2012.2198802","title":"Video Completion Using Bandlet Transform","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Motion compensation; Segmentation; Preprocessor; Object (grammar); Video tracking","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":[],"consensus_categories":[],"category_scores_codex":[0.0001771241,0.0001624534,0.0001545468,0.0001770858,0.000258979,0.00005196001,0.0002765336,0.00005618663,0.0001293512],"category_scores_gemma":[0.000004049015,0.0001535249,0.0001063758,0.0003164351,0.00004961816,0.00128367,0.000001414762,0.0002370251,0.0002305864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008546392,"about_ca_system_score_gemma":0.00002434286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002316825,"about_ca_topic_score_gemma":0.000004042124,"domain_scores_codex":[0.9988128,0.00004517027,0.0002159645,0.0002409223,0.0002807114,0.0004044133],"domain_scores_gemma":[0.9992568,0.0001088137,0.00004245232,0.0003318003,0.0000464001,0.0002136792],"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":[0.0000234139,0.0004116583,0.00005817228,0.00001355191,0.00002393895,0.000002989095,0.002414692,0.005930329,0.03063755,0.0001663116,0.0001490505,0.9601684],"study_design_scores_gemma":[0.0008253262,0.00004775653,0.0004287625,0.00003879298,0.00001700126,0.00003684205,0.00005646019,0.9146265,0.07743604,0.0001142994,0.006071123,0.0003011095],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003163842,0.00005561954,0.9939735,0.0003208639,0.001593415,0.0001698929,0.000007953385,0.000235389,0.0004795059],"genre_scores_gemma":[0.7495403,0.00001959045,0.2499021,0.0003454852,0.00007637199,0.00001055876,0.000001175499,0.00001343837,0.00009101166],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9598672,"threshold_uncertainty_score":0.6260565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03934462526742338,"score_gpt":0.3035480768918775,"score_spread":0.2642034516244541,"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."}}