{"id":"W2952123905","doi":"10.48550/arxiv.1110.5945","title":"A New Similarity Measure for Non-Local Means Filtering of MRI Images","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Similarity (geometry); Noise (video); Pattern recognition (psychology); Similarity measure; Measure (data warehouse); Filter (signal processing); Gaussian noise; Gaussian; Computer vision; Mathematics; Image (mathematics); Data mining","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":[0.002780971,0.0008011292,0.001519505,0.004099037,0.0006084786,0.002363309,0.001448011,0.002634063,0.001541822],"category_scores_gemma":[0.009865536,0.000301758,0.001288652,0.00281305,0.001551343,0.004077971,0.001760991,0.001511573,0.000866935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009878917,"about_ca_system_score_gemma":0.0007454494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005648978,"about_ca_topic_score_gemma":0.0005568349,"domain_scores_codex":[0.9958263,0.0007968405,0.0005063059,0.0008570859,0.001852951,0.0001604391],"domain_scores_gemma":[0.9958854,0.00165482,0.0006903303,0.0004937843,0.001103324,0.000172196],"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":[0.0004298038,0.0002908184,0.005054787,0.001274727,0.000607437,0.0006462745,0.0005733675,0.07531754,0.1029929,0.2170531,0.007102389,0.588657],"study_design_scores_gemma":[0.00004874286,0.001072157,0.01493499,0.0002115306,0.0002272631,0.003252436,0.0003119461,0.765228,0.03636763,0.1423858,0.03566477,0.0002946537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006036854,0.0008197201,0.9915702,0.0001168504,0.0001305449,0.00005057224,0.0000716007,0.0001008538,0.001102784],"genre_scores_gemma":[0.2413938,0.001744908,0.7500573,0.0004656392,0.0008038169,0.0004344526,0.0008209083,0.0001794572,0.004099671],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004099037,"threshold_uncertainty_score":0.01470739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1025979654820821,"score_gpt":0.2198036857844994,"score_spread":0.1172057203024173,"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."}}