{"id":"W2765376230","doi":"10.1109/avss.2017.8078533","title":"Multi-Scale histogram tone mapping algorithm enables better object detection in wide dynamic range images","year":2017,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Tone mapping; Histogram; Computer science; Artificial intelligence; Brightness; Pixel; Computer vision; High dynamic range; Scale (ratio); Contrast (vision); Pattern recognition (psychology); Face (sociological concept); Object detection; Consistency (knowledge bases); Algorithm; Dynamic range; 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":[0.0003858588,0.0004003879,0.000333018,0.0007929146,0.0002125569,0.0006682433,0.0004561989,0.0004034573,0.002616276],"category_scores_gemma":[0.001038122,0.0001624023,0.0003104566,0.0004889286,0.000253167,0.00104745,0.0004376307,0.0004159198,0.0007375905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001688494,"about_ca_system_score_gemma":0.0001708759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003691296,"about_ca_topic_score_gemma":0.0005708253,"domain_scores_codex":[0.9997913,0.00002902759,0.0000108805,0.0000499334,0.00009309981,0.00002585074],"domain_scores_gemma":[0.9995663,0.0001322157,0.00003910849,0.00009394093,0.0001424623,0.00002594417],"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":[0.0002964422,0.00009372089,0.001738896,0.0001744521,0.00004460983,0.0001444524,0.0001050035,0.0073683,0.2709941,0.002948702,0.001561876,0.7145295],"study_design_scores_gemma":[0.00004821418,0.0004699487,0.0131562,0.00003004847,0.0001009253,0.002309107,0.0001654977,0.4104988,0.5517045,0.003955577,0.01746597,0.00009511239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07251652,0.0004505178,0.923212,0.0001041785,0.0001080512,0.00007312699,0.00005742201,0.001198653,0.002279487],"genre_scores_gemma":[0.349609,0.0004909904,0.646406,0.000117684,0.00008159795,0.0000645104,0.0001457701,0.0001827558,0.00290161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002616276,"threshold_uncertainty_score":0.008752346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0132882572271697,"score_gpt":0.2779133304969855,"score_spread":0.2646250732698158,"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."}}