{"id":"W2019243801","doi":"10.1109/icip.2014.7025867","title":"Homogeneity classification for signal-dependent noise estimation in images","year":2014,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Gaussian noise; Artificial intelligence; Computer science; Homogeneity (statistics); Clipping (morphology); Pattern recognition (psychology); Noise measurement; Noise (video); Image noise; Poisson distribution; Computer vision; Mathematics; Statistics; Image (mathematics); Noise reduction; Machine learning","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":[0.001261354,0.00008572919,0.0001115969,0.0001140366,0.00007261093,0.0001492521,0.0003682289,0.00004620941,0.000008629419],"category_scores_gemma":[0.000161355,0.00007617556,0.00004132977,0.0001826221,0.00001701904,0.0004691628,0.00004997182,0.00005555791,0.00003151446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003730871,"about_ca_system_score_gemma":0.00002739197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000038264,"about_ca_topic_score_gemma":0.00001000842,"domain_scores_codex":[0.9990272,0.0001509464,0.0002040777,0.0002882479,0.0001578031,0.0001717446],"domain_scores_gemma":[0.9992131,0.0002762271,0.00006190942,0.0003170668,0.00009004144,0.00004163036],"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":[0.00002252101,0.0001123339,0.0005465748,0.00002956394,0.000004913087,0.000001574661,0.000204049,0.002557864,0.1509343,0.05582228,0.0009230832,0.7888409],"study_design_scores_gemma":[0.0005432825,0.00005458287,0.01350515,0.000007846156,0.000003192671,0.00000272686,0.000005850107,0.8524724,0.1110359,0.02199409,0.0002554648,0.0001195469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004612545,0.00001739498,0.9910181,0.0007392119,0.0001132168,0.0002055152,9.03118e-7,0.00009560869,0.003197517],"genre_scores_gemma":[0.5526271,0.00000137358,0.4466945,0.0001803594,0.00002255939,0.00002839039,0.000002923046,0.000003941719,0.0004388865],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8499146,"threshold_uncertainty_score":0.3106349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.033014064520356,"score_gpt":0.3067727769127327,"score_spread":0.2737587123923767,"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."}}