{"id":"W2610770792","doi":"10.1049/iet-ipr.2016.0489","title":"Convergent heterogeneous particle swarm optimisation algorithm for multilevel image thresholding segmentation","year":2017,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Thresholding; Image segmentation; Particle swarm optimization; Computer science; Image (mathematics); Segmentation; Artificial intelligence; Algorithm; Computer vision; Pattern recognition (psychology); Scale-space segmentation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005865185,0.0005447823,0.0008910801,0.0005433777,0.0003519979,0.0008083116,0.0007995516,0.001023033,0.001624626],"category_scores_gemma":[0.001438548,0.0002844537,0.0007039238,0.0005587427,0.0004263223,0.0004773485,0.0006204501,0.0007705472,0.0003290673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004414144,"about_ca_system_score_gemma":0.0007378627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004013826,"about_ca_topic_score_gemma":0.00264499,"domain_scores_codex":[0.999731,0.00005485287,0.000016554,0.00005844996,0.000111364,0.00002780015],"domain_scores_gemma":[0.9997126,0.0001329951,0.00003728271,0.0000212651,0.00008334108,0.00001250919],"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":[0.00009366612,0.00004317956,0.0009101591,0.0001657103,0.00007589134,0.0001357802,0.0001585877,0.87019,0.008949667,0.006958423,0.001619952,0.110699],"study_design_scores_gemma":[0.00000862373,0.00002041664,0.0001617428,0.000006828493,0.000007866686,0.00001851716,0.000009429198,0.9973645,0.0008953887,0.0008350183,0.0006675094,0.000004058857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01365537,0.0003483712,0.9815667,0.0001064874,0.00004354777,0.00004682611,0.00002302077,0.000284962,0.003924731],"genre_scores_gemma":[0.5225994,0.000566439,0.469133,0.0001181895,0.0000547223,0.0003150349,0.0002142308,0.0001153084,0.006883722],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004013826,"threshold_uncertainty_score":0.007980943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04206755606532445,"score_gpt":0.3486936152869593,"score_spread":0.3066260592216348,"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."}}