{"id":"W2007651542","doi":"10.1109/istas.2013.6613107","title":"High dynamic range tone mapping based on Per-Pixel Exposure Mapping","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Tone mapping; High dynamic range; Computer science; Compositing; High-dynamic-range imaging; Pixel; Computer vision; Artificial intelligence; Multiple exposure; Dynamic range; Set (abstract data type); Process (computing); Range (aeronautics); Tone (literature); Pairwise comparison; Computer graphics (images); Image (mathematics); Engineering","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":[0.0002266856,0.0004465771,0.0003366903,0.0004218601,0.0002010174,0.0005849211,0.0005668517,0.0002577524,0.003190555],"category_scores_gemma":[0.0007973843,0.0002262576,0.0002855144,0.00034459,0.0004138811,0.0008835719,0.0006419454,0.0005540063,0.0007047252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001898752,"about_ca_system_score_gemma":0.0002112225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003043636,"about_ca_topic_score_gemma":0.0004833019,"domain_scores_codex":[0.9997627,0.00003242712,0.000009338343,0.00004265368,0.0001328696,0.00001998217],"domain_scores_gemma":[0.9996258,0.0001220355,0.00003530425,0.0001137068,0.00007959601,0.00002351178],"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":[0.0002477555,0.000110721,0.0007867777,0.0001880098,0.0000280551,0.0002243085,0.0002114704,0.03186848,0.5720343,0.01778621,0.001206854,0.3753071],"study_design_scores_gemma":[0.00002805548,0.0003650204,0.001576287,0.00002468475,0.00003667279,0.001329015,0.00006495384,0.3977255,0.5822748,0.005011426,0.01150863,0.00005495857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05752879,0.0001975692,0.9361622,0.00004537954,0.00003861948,0.00007001066,0.00003092858,0.0007848775,0.005141593],"genre_scores_gemma":[0.3386084,0.0003262495,0.6546008,0.00005812541,0.00004663102,0.00007862943,0.0001075373,0.0002081497,0.005965445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003190555,"threshold_uncertainty_score":0.01067346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151662292635117,"score_gpt":0.2351100237603993,"score_spread":0.2235934008340481,"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."}}