{"id":"W2167254865","doi":"10.1109/icsmc.1989.71318","title":"Edge adaptive filtering: how much and which direction?","year":2003,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Smoothing; Impulse noise; Filter (signal processing); Artificial intelligence; Enhanced Data Rates for GSM Evolution; Computer science; Estimator; Orientation (vector space); Edge-preserving smoothing; Bilateral filter; Adaptive filter; Gaussian noise; Computer vision; Noise (video); Mathematics; Algorithm; Image (mathematics); Statistics; Pixel; Geometry","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":[0.001408178,0.0005528266,0.001014142,0.001102455,0.000310263,0.002314099,0.0008815352,0.001943602,0.005185746],"category_scores_gemma":[0.005150355,0.0003673429,0.0003546905,0.001580706,0.0009318538,0.004841321,0.0004165586,0.001542884,0.004241029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004997324,"about_ca_system_score_gemma":0.0005789652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001980426,"about_ca_topic_score_gemma":0.002896187,"domain_scores_codex":[0.9994953,0.0001036706,0.00003490739,0.0001001825,0.0002135677,0.00005239765],"domain_scores_gemma":[0.9983883,0.0003921273,0.0001467969,0.0001278335,0.0008270091,0.0001179776],"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":[0.0002077215,0.00007350864,0.002182564,0.0005269801,0.00006240364,0.00009536313,0.0001486763,0.00142678,0.01447031,0.02956359,0.03550705,0.9157351],"study_design_scores_gemma":[0.0002126722,0.0004162399,0.01481117,0.002904308,0.0004385519,0.002547398,0.002025569,0.124221,0.05613416,0.2850547,0.5107887,0.0004455588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02271732,0.07284876,0.8057331,0.05696992,0.005103878,0.0001323733,0.0005508113,0.001915318,0.03402834],"genre_scores_gemma":[0.2443551,0.08299711,0.5946684,0.01184992,0.005556404,0.0002056416,0.001008422,0.0009088461,0.05845016],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005185746,"threshold_uncertainty_score":0.01734805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03114037340702748,"score_gpt":0.2645257560809599,"score_spread":0.2333853826739324,"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."}}