{"id":"W1809620427","doi":"","title":"Invariant Image Improvement by sRGB colour space sharpening","year":2005,"lang":"en","type":"article","venue":"UEA Digital Repository (University of East Anglia)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Sharpening; Artificial intelligence; Computer vision; Invariant (physics); Mathematics; Color image; Color space; Computer science; Image processing; 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":[0.000369248,0.0005579027,0.0004312762,0.0009349403,0.000162724,0.0008632562,0.0005041403,0.0002980675,0.003167066],"category_scores_gemma":[0.0009449031,0.0002667791,0.0005013836,0.0007425128,0.0005749615,0.0009158677,0.0007244843,0.0006697263,0.001332295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000320853,"about_ca_system_score_gemma":0.0002298573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008519146,"about_ca_topic_score_gemma":0.000878815,"domain_scores_codex":[0.9997612,0.00002780544,0.00001168406,0.00005293673,0.0001037233,0.00004276445],"domain_scores_gemma":[0.9996124,0.00007799962,0.00005365142,0.0001397992,0.00009873739,0.00001742518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001674428,0.00004445154,0.0006397052,0.0001606381,0.00003011306,0.0001607022,0.0002226529,0.008836813,0.767594,0.00832998,0.001275321,0.2125381],"study_design_scores_gemma":[0.00002083422,0.0001293887,0.002814339,0.00001116479,0.00004636701,0.0006615738,0.00006740745,0.09241783,0.8918839,0.002542591,0.009362858,0.00004180071],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1520144,0.0004796705,0.8341517,0.0001610939,0.00005959092,0.00005484781,0.00009815761,0.003822967,0.009157593],"genre_scores_gemma":[0.3960292,0.0004949342,0.5956588,0.000108508,0.00002508662,0.0000287546,0.0001682494,0.0006271279,0.006859394],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003167066,"threshold_uncertainty_score":0.0105949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005327444168061117,"score_gpt":0.1750379053739109,"score_spread":0.1697104612058498,"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."}}