{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001115221,0.00004714672,0.00003910626,0.00004410652,0.00004895526,0.00006801679,0.0003151965,0.00001904239,0.0002149227],"category_scores_gemma":[0.00001248951,0.0000386931,0.00002319421,0.000175325,0.00001840371,0.0007767217,0.00006604748,0.00003335585,0.00021455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001735974,"about_ca_system_score_gemma":0.00001452853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002214241,"about_ca_topic_score_gemma":5.550653e-7,"domain_scores_codex":[0.9995512,0.00001663682,0.00009585654,0.0001406744,0.000102727,0.00009290579],"domain_scores_gemma":[0.999621,0.000009238916,0.00003444658,0.0002285821,0.00007535078,0.00003139938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003228657,0.0001178035,0.0008130019,0.00001361555,0.000009439607,0.000008480858,0.001884781,1.095896e-8,0.2765765,0.4710038,0.005560681,0.2440086],"study_design_scores_gemma":[0.00006439946,0.00003486959,0.006771336,0.000003278093,0.000001542845,0.000004807848,0.00005279155,0.002420173,0.9686447,0.02151955,0.0003829505,0.00009960208],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002028362,0.00001518349,0.8246676,0.0001235279,0.00004823431,0.00005552998,1.287698e-7,0.0004071775,0.1744798],"genre_scores_gemma":[0.3481292,0.000008730362,0.6499918,0.0002028911,0.00001194693,0.000009701969,6.705537e-7,0.000002951741,0.001642153],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6920682,"threshold_uncertainty_score":0.2757678,"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."}}