{"id":"W4206442543","doi":"10.18280/ts.380629","title":"Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Euclidean distance; Segmentation; Face (sociological concept); Artificial intelligence; Computer science; Pattern recognition (psychology); Human skin; Measure (data warehouse); Computer vision; Skin color; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001441653,0.0008842605,0.0007484854,0.002199554,0.00037534,0.001137944,0.000536613,0.0005442874,0.001307042],"category_scores_gemma":[0.004872985,0.0001759215,0.0005002219,0.001489254,0.0004187077,0.001049256,0.0005319177,0.0005016697,0.000718662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004054787,"about_ca_system_score_gemma":0.0003688021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001635998,"about_ca_topic_score_gemma":0.00196911,"domain_scores_codex":[0.9988507,0.0003517875,0.00008521618,0.0002429978,0.0003837543,0.00008545919],"domain_scores_gemma":[0.9980536,0.0007041956,0.0001558661,0.0002748672,0.0007215145,0.00009003703],"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":[0.001648277,0.0003215184,0.008488515,0.0004401157,0.000234921,0.0002895146,0.0003306626,0.1002581,0.103773,0.006774279,0.004220307,0.7732207],"study_design_scores_gemma":[0.00002881874,0.0004786813,0.009774339,0.00003010016,0.00005353187,0.0007905833,0.0002202911,0.9221894,0.06038297,0.00240051,0.003588393,0.00006238106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2203172,0.001502469,0.7737964,0.0001388731,0.0001933083,0.0001772669,0.000404184,0.001222207,0.002248056],"genre_scores_gemma":[0.4762111,0.0005288142,0.5208306,0.00004547831,0.00004086891,0.0001292935,0.0007824469,0.0001404307,0.001290948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002199554,"threshold_uncertainty_score":0.007624328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05870582638371101,"score_gpt":0.3269719322831284,"score_spread":0.2682661058994174,"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."}}