{"id":"W2096037605","doi":"10.1109/crv.2012.57","title":"Regularized Gradient Kernel Anisotropic Diffusion for Better Image Filtering","year":2012,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anisotropic diffusion; Edge-preserving smoothing; Smoothing; Kernel (algebra); Noise reduction; Artificial intelligence; Non-local means; Noise (video); Mathematics; Reproducing kernel Hilbert space; Computer vision; Filter (signal processing); Image (mathematics); Computer science; Pattern recognition (psychology); Algorithm; Image denoising; Hilbert space; Mathematical analysis","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":[0.0009154507,0.0005549354,0.0008980018,0.0005631407,0.0002277395,0.0006528333,0.0005339452,0.001003432,0.001032582],"category_scores_gemma":[0.002249023,0.0002473095,0.0008122245,0.0006001221,0.0005355618,0.001085234,0.0005138705,0.0009788106,0.0004724805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004351719,"about_ca_system_score_gemma":0.0005118057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001958482,"about_ca_topic_score_gemma":0.002097938,"domain_scores_codex":[0.999563,0.0001041026,0.00002944792,0.00007973558,0.0001910578,0.00003272813],"domain_scores_gemma":[0.999405,0.0001562649,0.00006476829,0.0001511498,0.0001997248,0.00002301783],"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":[0.0002809048,0.0001172101,0.000729579,0.0003141566,0.0001739278,0.0002209818,0.0002643393,0.2616228,0.2570384,0.1217268,0.00401731,0.3534937],"study_design_scores_gemma":[0.00001176695,0.00002977302,0.0002361656,0.000007526335,0.00001835489,0.00009479639,0.000008449803,0.9707407,0.01514742,0.008383972,0.005301767,0.00001935282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006963703,0.0002490294,0.9918448,0.00009153428,0.00003758555,0.00001066629,0.00001574337,0.0002031191,0.0005838224],"genre_scores_gemma":[0.1597444,0.0006558746,0.8360464,0.000106625,0.00009061117,0.00004401723,0.0001218588,0.0001486377,0.003041517],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001958482,"threshold_uncertainty_score":0.004841447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02344083908195707,"score_gpt":0.2756899738941359,"score_spread":0.2522491348121788,"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."}}