{"id":"W2000992950","doi":"10.1109/ictai.2012.57","title":"Random Forests Based View Generation for Multiview TV","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Telus (Canada); University of British Columbia","funders":"","keywords":"Computer science; Depth map; Artificial intelligence; Computer vision; Monocular; Depth perception; View synthesis; Pipeline (software); 2D to 3D conversion; Depth of field; Bandwidth (computing); Random forest; Quality (philosophy); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000842046,0.0007517124,0.000724417,0.001094788,0.0002958073,0.0004329489,0.00127542,0.000830857,0.002261336],"category_scores_gemma":[0.001965131,0.0004785018,0.001073389,0.00063134,0.0002519056,0.0006407214,0.0006695954,0.0009753866,0.0007889505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005512693,"about_ca_system_score_gemma":0.0005403156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005518122,"about_ca_topic_score_gemma":0.008955006,"domain_scores_codex":[0.9995425,0.0001093023,0.000018768,0.0001221324,0.0001440783,0.00006324321],"domain_scores_gemma":[0.9992204,0.0003604337,0.00007588643,0.000122652,0.0001780317,0.00004258873],"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":[0.0003835389,0.0001467863,0.001357523,0.00009704317,0.00008823452,0.0001366206,0.00007866608,0.3268261,0.0381683,0.003052216,0.005444963,0.62422],"study_design_scores_gemma":[0.00001118303,0.00001836689,0.0002313724,0.000003485123,0.000007864893,0.00004781433,0.00000632665,0.9934847,0.004147167,0.001526718,0.0005068366,0.000008133697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01123516,0.0001472348,0.9869687,0.00004578515,0.00002400164,0.00003037033,0.0001192277,0.001054507,0.0003751311],"genre_scores_gemma":[0.3224625,0.0001850992,0.6743392,0.0001101957,0.00007446286,0.0001106641,0.0009624423,0.0002704708,0.001484961],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005518122,"threshold_uncertainty_score":0.01097196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05513453109603764,"score_gpt":0.3373699834684127,"score_spread":0.2822354523723751,"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."}}