{"id":"W2063694967","doi":"10.1142/s0219467805001732","title":"SHOCK FILTER-BASED DIFFUSION FIELDS — APPLICATION TO GRAYSCALE CHARACTER IMAGE PROCESSING","year":2005,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Grayscale; Character (mathematics); Computer science; Artificial intelligence; Image processing; Computer vision; Anisotropic diffusion; Image (mathematics); Diffusion; Noise (video); Spurious relationship; Partial differential equation; Algorithm; Pattern recognition (psychology); Mathematics; Geometry; Mathematical analysis","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":[0.000280204,0.0002962696,0.0002833245,0.0005896655,0.0002085814,0.000528844,0.0002571628,0.0005647498,0.001287488],"category_scores_gemma":[0.0007589491,0.0001362046,0.0002614524,0.0005151419,0.0003514699,0.0005646365,0.0003221246,0.0003739428,0.000205754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004083632,"about_ca_system_score_gemma":0.0002637624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001271608,"about_ca_topic_score_gemma":0.0007866151,"domain_scores_codex":[0.9999369,0.0000134073,0.000004551656,0.00001030547,0.00002825755,0.00000658165],"domain_scores_gemma":[0.9998167,0.00007592325,0.0000200089,0.00001370674,0.00005995493,0.00001376179],"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":[0.0002629461,0.00009209437,0.001312615,0.0002614939,0.0000425352,0.0005271872,0.0003169017,0.3000756,0.2212558,0.08250021,0.001932499,0.3914202],"study_design_scores_gemma":[0.00001365312,0.00004882657,0.0003775205,0.0000103882,0.000007101782,0.0001646876,0.000024922,0.9663064,0.02370268,0.006247075,0.003082202,0.00001460307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03397991,0.0003138679,0.9632313,0.0002396187,0.00005995961,0.00004071916,0.00002607219,0.0002777479,0.001830797],"genre_scores_gemma":[0.5331106,0.001307212,0.4549682,0.0001343127,0.00007693676,0.00006863675,0.0000857131,0.00008919137,0.01015919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001287488,"threshold_uncertainty_score":0.004307091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004887411021763783,"score_gpt":0.2587523063273885,"score_spread":0.2538648953056248,"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."}}