{"id":"W4310382621","doi":"10.5566/ias.2812","title":"Feature Extraction for Patch Matching in Patch-based Denoising Methods","year":2022,"lang":"en","type":"article","venue":"Image Analysis & Stereology","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Pattern recognition (psychology); Noise reduction; Feature (linguistics); Computer science; Matching (statistics); Image (mathematics); Noise (video); Mathematics; Euclidean distance; Computer vision; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007641896,0.0005385836,0.001221772,0.001659113,0.0003612689,0.0007358691,0.001055693,0.0009842897,0.002461424],"category_scores_gemma":[0.002824466,0.0004301721,0.001052397,0.001621543,0.0004870053,0.001238527,0.0008929669,0.0007832063,0.00139811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003627425,"about_ca_system_score_gemma":0.0003963647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001233411,"about_ca_topic_score_gemma":0.001296081,"domain_scores_codex":[0.9992513,0.00009199161,0.00005192408,0.0002408447,0.0002961025,0.00006776687],"domain_scores_gemma":[0.9991335,0.0002058707,0.00008311181,0.0002387416,0.000304409,0.00003435412],"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":[0.0001985997,0.0001076984,0.001214832,0.0002136746,0.0001038017,0.000114805,0.0001295985,0.02453909,0.1695198,0.004436148,0.002760934,0.796661],"study_design_scores_gemma":[0.00003384523,0.0002104537,0.005125109,0.00003047804,0.0001092553,0.0006415414,0.00008612969,0.8505583,0.1230276,0.007954452,0.01216933,0.00005349434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007232245,0.0001408954,0.9916676,0.00002264633,0.00002212979,0.00003809319,0.00003353889,0.0004942504,0.0003485771],"genre_scores_gemma":[0.1110133,0.0002951929,0.8865051,0.00006327582,0.00004884645,0.0001068263,0.0003211693,0.0003037126,0.001342585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002461424,"threshold_uncertainty_score":0.008234322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02312051824937284,"score_gpt":0.3722593587346553,"score_spread":0.3491388404852825,"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."}}