{"id":"W2111467839","doi":"10.1109/ccece.2008.4564583","title":"Novel artifact detection for motion compensated deinterlacing","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Flicker; Motion compensation; Artifact (error); Motion estimation; Quarter-pixel motion; Motion (physics); Interpolation (computer graphics); Trajectory; Motion interpolation; Motion detection; Compensation (psychology); Computer graphics (images); Block-matching algorithm; Video processing; Video tracking","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005091635,0.000771784,0.0005745556,0.0009372717,0.0003040663,0.0005142993,0.0009206841,0.0007059809,0.001184335],"category_scores_gemma":[0.001852666,0.0002569743,0.0003905983,0.000746656,0.0003635171,0.0009063951,0.0005957953,0.00059782,0.0005831312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002855354,"about_ca_system_score_gemma":0.0003776723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005050356,"about_ca_topic_score_gemma":0.0006901717,"domain_scores_codex":[0.9994689,0.00007799623,0.00003462461,0.00008734697,0.000291463,0.0000396975],"domain_scores_gemma":[0.9990262,0.0002294553,0.0001150611,0.0001541869,0.0004340274,0.0000412226],"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":[0.0005895115,0.0001010758,0.001224767,0.0002714966,0.00005265149,0.0003216215,0.0001104406,0.008812397,0.3917359,0.004681242,0.001361709,0.5907372],"study_design_scores_gemma":[0.00007387677,0.0005306563,0.003160893,0.00004954151,0.00009080406,0.002189692,0.00004404939,0.5584086,0.4179906,0.002067117,0.01531582,0.00007838594],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0186748,0.0005043708,0.9793766,0.00005604772,0.00008555961,0.00005808376,0.00002621681,0.0004488012,0.0007694834],"genre_scores_gemma":[0.194657,0.0008398931,0.8008232,0.0001244949,0.0000967834,0.00007586827,0.0001611283,0.00006854651,0.003153031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001184335,"threshold_uncertainty_score":0.00396204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0321734131088954,"score_gpt":0.2037978845321163,"score_spread":0.1716244714232209,"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."}}