{"id":"W2889231032","doi":"10.1109/ccece.2018.8447795","title":"Image Segmentation Using Inverted Dirichlet Mixture Model and Spatial Information","year":2018,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Image segmentation; Artificial intelligence; Pattern recognition (psychology); Markov random field; Expectation–maximization algorithm; Dirichlet distribution; Computer science; Scale-space segmentation; Segmentation-based object categorization; Mixture model; Pixel; Segmentation; Computer vision; Algorithm; Mathematics; Maximum likelihood; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001803794,0.00008710008,0.00007863343,0.00007689615,0.0001094756,0.0001870228,0.0001493516,0.00005558927,0.00001014969],"category_scores_gemma":[0.00001449062,0.0000715635,0.00001657159,0.0001470798,0.00004226733,0.00172754,0.0001114221,0.0000540615,0.00001036707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001914086,"about_ca_system_score_gemma":0.00003596898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001697431,"about_ca_topic_score_gemma":0.00003256976,"domain_scores_codex":[0.9994147,0.00003934465,0.000145474,0.0001389579,0.0001309061,0.0001306188],"domain_scores_gemma":[0.9995767,0.0000119854,0.00005882384,0.0001827371,0.0001101232,0.00005962035],"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":[0.00001609061,0.00002467796,0.0001019235,0.00003178192,0.00001695333,0.000001242709,0.005281909,0.0000840901,0.07340395,0.0375118,0.00308394,0.8804417],"study_design_scores_gemma":[0.0002098286,0.00002642483,0.0001343132,0.000004758194,0.000004684449,0.00000736705,0.000008130931,0.9763187,0.005371242,0.01773241,0.00008432821,0.00009779255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008989355,0.000007290504,0.9874768,0.0002739999,0.0001130553,0.0001158991,0.000001816394,0.00007874015,0.002943039],"genre_scores_gemma":[0.1599481,0.000003192091,0.8386849,0.001268865,0.00004672385,0.000002051567,0.000003439835,0.000002836844,0.00003991545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9762346,"threshold_uncertainty_score":0.2918275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01861376912921879,"score_gpt":0.2773886380187554,"score_spread":0.2587748688895365,"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."}}