{"id":"W1510047790","doi":"10.1109/tip.2015.2456505","title":"Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Artificial intelligence; Image segmentation; Pattern recognition (psychology); Pixel; Cluster analysis; Range segmentation; Computer science; Fuzzy clustering; Smoothing; Scale-space segmentation; Segmentation-based object categorization; Computer vision; Fuzzy logic; Region growing; Segmentation; 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":[0.0008809239,0.0006570052,0.0007788605,0.001813227,0.0005760289,0.0005872645,0.001176084,0.0009646777,0.0006843529],"category_scores_gemma":[0.002180414,0.0004248522,0.0008373384,0.001358325,0.000659088,0.001040364,0.0007691251,0.000596567,0.0002823785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102012,"about_ca_system_score_gemma":0.0009569114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006496937,"about_ca_topic_score_gemma":0.008679034,"domain_scores_codex":[0.9993002,0.0001090864,0.00004145249,0.0001739829,0.0003247656,0.00005047023],"domain_scores_gemma":[0.9993683,0.0002516274,0.00008513277,0.00007341605,0.0001977244,0.00002373449],"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":[0.0001236219,0.00008336696,0.001090375,0.0002209473,0.0001126465,0.0001203947,0.0002680923,0.4912356,0.06790531,0.0093153,0.001182251,0.4283421],"study_design_scores_gemma":[0.000004172914,0.00002349788,0.0004152417,0.000007821895,0.00001808933,0.000038836,0.00001482031,0.9878374,0.008384434,0.002465848,0.00077226,0.00001748794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006176814,0.0001481382,0.9930254,0.00002420957,0.00000753015,0.00001801503,0.000009091067,0.0002241048,0.0003666844],"genre_scores_gemma":[0.2230866,0.0003010979,0.7752935,0.00006716442,0.00004282177,0.00009099018,0.00009119068,0.0001252379,0.0009013473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006496937,"threshold_uncertainty_score":0.01291823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02449092549421801,"score_gpt":0.2607718656546555,"score_spread":0.2362809401604375,"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."}}