{"id":"W176198518","doi":"","title":"3D-TV: coding of disocclusions for 2D+depth representation of multi-view images","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"View synthesis; Pixel; Computer science; Computer vision; Embedding; Rendering (computer graphics); Artificial intelligence; Coding (social sciences); ENCODE; Wavelet; Exploit; Computer graphics (images); Depth map; Mathematics; Image (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001118045,0.00008059003,0.0002136841,0.0001139008,0.0001403554,0.00001087419,0.0006405173,0.0000478566,0.000008458545],"category_scores_gemma":[0.0002399808,0.00006226468,0.00009499775,0.0003296386,0.00009582989,0.0002108084,0.0003958117,0.00005140219,0.000002150297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008117378,"about_ca_system_score_gemma":0.00002954652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006134554,"about_ca_topic_score_gemma":0.000005035957,"domain_scores_codex":[0.9991564,0.00002885089,0.0002931428,0.0002318004,0.000157896,0.0001318708],"domain_scores_gemma":[0.998938,0.0002215336,0.0001697216,0.0005063693,0.0001399763,0.0000244034],"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":[0.00003217393,0.0007201205,0.01626072,0.0003635157,0.00008766636,0.00001023107,0.002798554,0.0009938012,0.22993,0.07980355,0.02370792,0.6452917],"study_design_scores_gemma":[0.0008368694,0.0001717716,0.01026103,0.0001874715,0.0000133864,0.0000152962,0.0003457394,0.1041137,0.8795739,0.003540119,0.0007212031,0.0002195051],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0186192,0.0003607694,0.9790832,0.000445965,0.0001044376,0.0001870164,0.000004072182,0.0002621355,0.0009332193],"genre_scores_gemma":[0.7825167,0.0003910813,0.2165426,0.00002328939,0.000005726977,0.00001936724,0.000001241514,0.000003618979,0.0004963405],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7638975,"threshold_uncertainty_score":0.253908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09064010294458837,"score_gpt":0.3364972430847718,"score_spread":0.2458571401401834,"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."}}