{"id":"W2121665983","doi":"10.1007/978-3-642-24965-5_58","title":"Spatial Finite Non-gaussian Mixture for Color Image Segmentation","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Mixture model; Image segmentation; Pattern recognition (psychology); Segmentation; Computer vision; Expectation–maximization algorithm; Scale-space segmentation; Maximization; Pixel; Gaussian; Image (mathematics); Maximum likelihood; Mathematics; Mathematical optimization; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001072538,0.0006750393,0.0006622756,0.0006832482,0.0003354379,0.000555463,0.002924113,0.0005286374,0.00003452393],"category_scores_gemma":[0.00009288228,0.0005925288,0.0002447573,0.0003873993,0.0005138184,0.0007654296,0.000793464,0.0006993196,0.00003309866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002125173,"about_ca_system_score_gemma":0.0005646624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004422144,"about_ca_topic_score_gemma":0.00008922633,"domain_scores_codex":[0.9960658,0.00005166369,0.0005994379,0.001809347,0.0006738531,0.0007999025],"domain_scores_gemma":[0.9971246,0.0005530943,0.0004238823,0.001324645,0.0003302091,0.0002435992],"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":[0.00002366996,0.00003311769,0.000003756873,0.00007369736,0.00001621596,0.00005304472,0.001341148,0.0004490505,0.001054005,0.027048,0.0000991577,0.9698051],"study_design_scores_gemma":[0.000601928,0.000393288,0.00003381184,0.0002450495,0.00002296677,0.00004171391,9.273654e-8,0.4993938,0.008955447,0.4882901,0.001193124,0.000828692],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000006359151,0.0001465744,0.9907213,0.0006686804,0.002603984,0.001158762,0.00002794792,0.0001310179,0.004535365],"genre_scores_gemma":[0.01030984,0.00002943072,0.9862833,0.001792175,0.000733218,0.00005576509,0.00001711729,0.00005264999,0.0007265108],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9689764,"threshold_uncertainty_score":0.9996526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01966009758619449,"score_gpt":0.2684416241560063,"score_spread":0.2487815265698118,"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."}}