{"id":"W2169384954","doi":"10.1109/fuzzy.2011.6007601","title":"Evolving fuzzy image segmentation","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Image segmentation; Computer vision; Computer science; Image texture; Pixel; Scale-space segmentation; Segmentation; Segmentation-based object categorization; Range segmentation; Pattern recognition (psychology); Minimum spanning tree-based segmentation; Fuzzy logic; Region growing; Process (computing)","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":[0.0007522907,0.0004416874,0.0007415819,0.00104757,0.0006290981,0.0008843148,0.001108269,0.0009962782,0.001915503],"category_scores_gemma":[0.002139597,0.0002817035,0.000637146,0.0007585678,0.0007353612,0.0009189134,0.0007088374,0.0004978221,0.000370537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204313,"about_ca_system_score_gemma":0.0005903291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004992596,"about_ca_topic_score_gemma":0.003060501,"domain_scores_codex":[0.9995139,0.00005677909,0.00002989261,0.0001545796,0.0002046442,0.00004015634],"domain_scores_gemma":[0.999315,0.0001795142,0.00007536968,0.00009011352,0.0002989375,0.0000410028],"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":[0.0002026239,0.0001074493,0.003307451,0.0001665755,0.0001160317,0.0004425148,0.0005433363,0.444938,0.08501054,0.01633644,0.002024931,0.446804],"study_design_scores_gemma":[0.000006839943,0.0000666728,0.001005281,0.0000127084,0.00002045218,0.0001622522,0.00004653473,0.9778526,0.01392851,0.004293566,0.002587147,0.00001752828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0959197,0.0003403414,0.8955562,0.0001266374,0.00005292102,0.0001106463,0.00006974391,0.0007416172,0.007082283],"genre_scores_gemma":[0.6534355,0.0002758069,0.3388965,0.00008733523,0.00003057798,0.0001098183,0.0001949635,0.00008259977,0.006886791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004992596,"threshold_uncertainty_score":0.009927094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0340798942412341,"score_gpt":0.2588566184431587,"score_spread":0.2247767242019246,"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."}}