{"id":"W2097048977","doi":"10.1145/2461912.2461925","title":"Adaptive image synthesis for compressive displays","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Optical Imaging Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; Alfred P. Sloan Foundation","keywords":"Computer science; Rendering (computer graphics); High dynamic range; Computer vision; Stereoscopy; Computer graphics (images); Artificial intelligence; Light field; Compressed sensing; Set (abstract data type); Global illumination; Dynamic range","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003580109,0.0004085547,0.00030148,0.0002352442,0.0001741294,0.0005059246,0.0003967401,0.0004549873,0.002609717],"category_scores_gemma":[0.001182676,0.0001858164,0.0003418872,0.0002435382,0.0006086476,0.0006183058,0.0008205653,0.0008421848,0.000422882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003696109,"about_ca_system_score_gemma":0.0003086525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007481335,"about_ca_topic_score_gemma":0.0007967633,"domain_scores_codex":[0.9997239,0.00007770323,0.000009876559,0.00004577636,0.0001259461,0.00001684866],"domain_scores_gemma":[0.9996501,0.0002156682,0.00002859293,0.00004336361,0.00004772159,0.00001462792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002521057,0.00007281647,0.0004188529,0.0002269675,0.00005174612,0.0001039635,0.000180723,0.5071102,0.1422538,0.1239414,0.003866006,0.2215215],"study_design_scores_gemma":[0.00001858838,0.00003744936,0.00008484479,0.000009685057,0.00000550298,0.00004098668,0.00001093168,0.971611,0.0112388,0.01353625,0.003396175,0.000009750122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006182687,0.0002409439,0.9910762,0.0001366604,0.00002181842,0.0000161267,0.00002892467,0.000189854,0.002106622],"genre_scores_gemma":[0.3792928,0.001085949,0.6120378,0.0002650058,0.0001984752,0.0001554491,0.0001850575,0.0001840499,0.006595462],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002609717,"threshold_uncertainty_score":0.008730412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0176236540023535,"score_gpt":0.2382808043778286,"score_spread":0.2206571503754751,"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."}}