{"id":"W1994981962","doi":"10.1109/ccca.2011.6031397","title":"A probabilistic algorithm for spatial color image segmentation","year":2011,"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; Scale-space segmentation; Artificial intelligence; Mixture model; Segmentation-based object categorization; Pattern recognition (psychology); Computer science; Segmentation; Minimum spanning tree-based segmentation; Computer vision; Region growing; Image texture; Statistical model; Algorithm; Mathematics","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":[0.001456099,0.001337883,0.001634496,0.003199264,0.001399111,0.00190796,0.002788658,0.002545696,0.007569167],"category_scores_gemma":[0.003362706,0.001257035,0.002118798,0.003443388,0.001348976,0.002462035,0.002694451,0.00218319,0.004179331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001430642,"about_ca_system_score_gemma":0.001816852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004862205,"about_ca_topic_score_gemma":0.004836113,"domain_scores_codex":[0.9986185,0.0002119849,0.00009653847,0.000390824,0.0005792683,0.0001028344],"domain_scores_gemma":[0.999062,0.0003207423,0.00007820278,0.0001543242,0.0003376615,0.00004701655],"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":[0.0001257816,0.00006739566,0.0004082165,0.0001888147,0.00009500568,0.0001493206,0.0001834626,0.1429656,0.02018587,0.04616126,0.008577931,0.7808914],"study_design_scores_gemma":[0.000028586,0.00003332284,0.0002064278,0.0000249351,0.00003010801,0.0003054424,0.00003626099,0.9445711,0.008334292,0.03152668,0.01485786,0.00004483189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000216711,0.00005357338,0.9989069,0.00002745414,0.00001337959,0.00002112159,0.00001399655,0.0004942165,0.0002526642],"genre_scores_gemma":[0.009153288,0.0001223347,0.9891433,0.00004845237,0.00002089004,0.0001253742,0.0001135753,0.0001550887,0.001117633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007569167,"threshold_uncertainty_score":0.02532136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03325165093396945,"score_gpt":0.2798878962967041,"score_spread":0.2466362453627347,"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."}}