{"id":"W2971615301","doi":"10.1109/access.2019.2939229","title":"A Global Optimization Method for Specular Highlight Removal From a Single Image","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Specular reflection; Computer vision; Artificial intelligence; Computer science; Chromaticity; Specular highlight; Hue; Pattern recognition (psychology); Optics","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":[0.0008567427,0.001727688,0.00100276,0.0006297652,0.000296223,0.0006982021,0.0008748919,0.001118851,0.002154271],"category_scores_gemma":[0.001279627,0.0006177176,0.001195879,0.0005976714,0.0006323734,0.0007209019,0.0008831262,0.001289086,0.0008800099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000502353,"about_ca_system_score_gemma":0.001308178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003361882,"about_ca_topic_score_gemma":0.003939363,"domain_scores_codex":[0.9996039,0.00008883455,0.00001947231,0.0001073947,0.0001398045,0.0000406091],"domain_scores_gemma":[0.9996597,0.0001489591,0.00004522159,0.00003366351,0.00009130275,0.00002124297],"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":[0.000147638,0.00008203885,0.0005468391,0.0002933353,0.0001603837,0.0001544614,0.000126707,0.7410082,0.04185444,0.007817844,0.006159631,0.2016485],"study_design_scores_gemma":[0.00001001193,0.00003613012,0.0001408923,0.000008532463,0.00001319371,0.00004643098,0.00001029258,0.9947888,0.002614641,0.001139251,0.001180078,0.00001180225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002421248,0.000113508,0.9966347,0.00004912645,0.00001408334,0.00001385636,0.00002624864,0.0002072857,0.0005199902],"genre_scores_gemma":[0.08925522,0.0004420676,0.9042315,0.0001653987,0.0000779728,0.0001943993,0.0004254512,0.0004920895,0.004715866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003361882,"threshold_uncertainty_score":0.007206738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01725701255720994,"score_gpt":0.3316330528290188,"score_spread":0.3143760402718089,"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."}}