{"id":"W3028989134","doi":"10.1109/lsp.2020.3043990","title":"Multi-Volumetric Refocusing of Light Fields","year":2020,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Victoria","funders":"","keywords":"Finite impulse response; Filter (signal processing); Computer science; Reduction (mathematics); Filter design; Algorithm; Planar; Light field; Computer vision; Artificial intelligence; Mathematics; Geometry","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":[0.0001047361,0.0001285965,0.0001792765,0.0001346202,0.0001163874,0.000116672,0.0005955765,0.00003578883,0.000007754154],"category_scores_gemma":[0.00003822628,0.0001182666,0.00006293474,0.0009544555,0.00004294325,0.0007502819,0.00009128588,0.0002033736,0.00001683072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002005923,"about_ca_system_score_gemma":0.00004262635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004190572,"about_ca_topic_score_gemma":1.573584e-7,"domain_scores_codex":[0.9988306,0.00003274083,0.0002654021,0.0003505036,0.000281454,0.0002393615],"domain_scores_gemma":[0.9994529,0.00003377114,0.0001639176,0.0001671918,0.00007164043,0.0001106074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006725108,0.00002549636,0.0001379575,0.00008399571,0.000005871703,0.0000252817,0.00122983,0.001304325,0.6671295,0.00001101101,0.001378573,0.3286614],"study_design_scores_gemma":[0.0004842885,0.00006020008,0.0001750312,0.0001379329,0.000007314281,0.00000881153,0.00003600724,0.7896266,0.2067457,0.00005171143,0.002389496,0.0002768937],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007578756,0.0002813314,0.9783386,0.01334275,0.0001186939,0.00005588053,3.418194e-7,0.0001413615,0.0001423078],"genre_scores_gemma":[0.7581096,0.000002097872,0.2317977,0.009979642,0.00008634844,0.000001327805,2.077328e-7,0.000009635047,0.00001343173],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7883223,"threshold_uncertainty_score":0.4822772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03168523418857924,"score_gpt":0.2721402795135794,"score_spread":0.2404550453250002,"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."}}