{"id":"W2050262943","doi":"10.1109/cvpr.2011.5995335","title":"Glare encoding of high dynamic range images","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"High dynamic range; Computer science; Computer vision; Dynamic range; High-dynamic-range imaging; Computational photography; Encoding (memory); Artificial intelligence; ENCODE; Range (aeronautics); Image sensor; Tone mapping; Photography; Computer graphics (images); Filter (signal processing); Software; Image (mathematics); Image processing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006813971,0.00005562554,0.00008415064,0.00005775501,0.00003222304,0.00001399578,0.0003941781,0.00001230684,0.0001636037],"category_scores_gemma":[0.00001513484,0.00004446654,0.00002929212,0.0001374454,0.0000249528,0.0004852983,0.0001588509,0.00004163845,0.00003412691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008399246,"about_ca_system_score_gemma":0.000008978953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004602305,"about_ca_topic_score_gemma":0.00000271087,"domain_scores_codex":[0.9995118,0.00001232427,0.0001107234,0.0001526373,0.0000947297,0.0001177429],"domain_scores_gemma":[0.9995875,0.00001943347,0.00004199444,0.0002741616,0.00004248253,0.000034385],"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":[0.00001062398,0.0001278313,0.003792755,0.00004018555,0.00001582449,0.00003900313,0.002601422,0.000009446898,0.03062069,0.1494843,0.001068685,0.8121892],"study_design_scores_gemma":[0.002474225,0.0003478043,0.146584,0.0002269929,0.00001790314,0.00007857713,0.000853347,0.2876499,0.4501171,0.107173,0.003297866,0.001179312],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004282606,0.00007400614,0.970259,0.0001294408,0.0001392091,0.00003617763,7.023411e-7,0.0001031443,0.02497573],"genre_scores_gemma":[0.6755857,0.00001156746,0.3238287,0.0001083591,0.000003262818,8.505143e-7,1.843385e-7,0.000002441933,0.000458938],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8110099,"threshold_uncertainty_score":0.1813293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197513352008232,"score_gpt":0.2604865514338578,"score_spread":0.2407352162330346,"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."}}