{"id":"W2083592092","doi":"10.1109/nafips.2006.365414","title":"Perceptual Colour Image Sharpening using Fuzzy Morphological Agents","year":2006,"lang":"en","type":"article","venue":"","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Sharpening; Artificial intelligence; Computer vision; RGB color model; Computer science; Fuzzy logic; Image (mathematics); RGB color space; Space (punctuation); Perception; Chromaticity; Pattern recognition (psychology); Image processing; Mathematics; Color image","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005900481,0.00007245814,0.0000747634,0.00002112413,0.0001982829,0.00007232334,0.0001420634,0.00001744202,0.004560301],"category_scores_gemma":[9.625177e-7,0.00006017875,0.00005362854,0.0001459448,0.00006221833,0.0001250077,0.00006633542,0.00006284231,0.0003018395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001385829,"about_ca_system_score_gemma":0.00001988505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006225958,"about_ca_topic_score_gemma":0.000005065872,"domain_scores_codex":[0.9993972,0.000009594559,0.0001140504,0.0001871663,0.00009625818,0.0001957242],"domain_scores_gemma":[0.9997723,0.00001567371,0.00003151642,0.0001143188,0.00002504332,0.00004119144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000009556907,0.0007253618,0.2122478,0.000002997415,0.0000219636,0.00001680443,0.0002989928,0.001868009,0.4886917,0.2455523,0.04278431,0.007780191],"study_design_scores_gemma":[0.002874753,0.0002279572,0.6335287,0.00003410758,0.000157276,0.00004226595,0.01035925,0.1502387,0.0349628,0.1076284,0.05761234,0.002333419],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8819351,0.000001355542,0.01309334,0.0001410015,0.00002586432,0.0000902874,0.000009056464,0.00003001309,0.104674],"genre_scores_gemma":[0.9893101,6.012959e-8,0.00848034,0.00007490216,0.0001831539,0.00001441358,0.00001832279,0.000004391311,0.001914295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4537289,"threshold_uncertainty_score":0.9963497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03159453691600567,"score_gpt":0.3001923827546299,"score_spread":0.2685978458386243,"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."}}