{"id":"W1968736717","doi":"10.1049/iet-ipr.2013.0232","title":"Image segmentation by Dirichlet process mixture model with generalised mean","year":2014,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Priority Academic Program Development of Jiangsu Higher Education Institutions; Deutsche Forschungsgemeinschaft","keywords":"Image segmentation; Artificial intelligence; Process (computing); Computer science; Segmentation; Pattern recognition (psychology); Dirichlet process; Mean-shift; Hierarchical Dirichlet process; Mathematics; Computer vision; Latent Dirichlet allocation; Topic model","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":[0.00201913,0.001313324,0.002370163,0.002619074,0.0007934939,0.001899532,0.002293995,0.002939821,0.0022309],"category_scores_gemma":[0.003836299,0.001619279,0.003757071,0.00300228,0.001325902,0.002466482,0.001923637,0.002225754,0.001242639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001499895,"about_ca_system_score_gemma":0.001286351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01000616,"about_ca_topic_score_gemma":0.007303898,"domain_scores_codex":[0.9978174,0.0007530177,0.000119613,0.0006368071,0.0005278971,0.0001451766],"domain_scores_gemma":[0.9990042,0.0006038087,0.00009687962,0.0001056556,0.0001519605,0.00003761351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002753916,0.00005960967,0.000915656,0.000344733,0.0002208165,0.0002610491,0.0003731044,0.7607221,0.01355105,0.03390285,0.004288531,0.1850852],"study_design_scores_gemma":[0.000007517395,0.000009592859,0.0001151,0.000008138016,0.00001494281,0.00004818027,0.000007624352,0.9889733,0.001217068,0.008548391,0.001029918,0.0000201796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002340082,0.0003871605,0.996021,0.0001065458,0.00002648448,0.00002923895,0.00005246964,0.0005372322,0.0004998341],"genre_scores_gemma":[0.1773273,0.001401146,0.8133396,0.0003243659,0.0001635609,0.0004221197,0.000926486,0.0006857939,0.005409632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01000616,"threshold_uncertainty_score":0.01989585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009403873022496807,"score_gpt":0.2716601292637656,"score_spread":0.2622562562412689,"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."}}