{"id":"W1942068614","doi":"10.1155/2015/240354","title":"A Color Texture Image Segmentation Method Based on Fuzzy c-Means Clustering and Region-Level Markov Random Field Model","year":2015,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Southwest Forestry University; China Scholarship Council; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Artificial intelligence; Markov random field; Pattern recognition (psychology); Cluster analysis; Fuzzy clustering; Image segmentation; Fuzzy logic; Image texture; Computer vision; Computer science; Pixel; Mathematics; Segmentation","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005923568,0.0002609946,0.0003206196,0.0002129951,0.00002596321,0.0000852399,0.0001098612,0.000154969,0.000003437576],"category_scores_gemma":[0.0004925822,0.0002557357,0.00004540533,0.0002027104,0.00001930445,0.0001948933,0.00003381901,0.0003263898,0.000009552884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001898457,"about_ca_system_score_gemma":0.00001832672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003262678,"about_ca_topic_score_gemma":0.000003750151,"domain_scores_codex":[0.9987487,0.00003651165,0.00041377,0.000263271,0.0002372613,0.0003004448],"domain_scores_gemma":[0.9990642,0.000430175,0.00004126103,0.0002873844,0.00004275472,0.0001341901],"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":[0.00002231642,0.0000271588,0.000005765451,0.000698601,0.00001042156,0.000008588951,0.0006030793,0.9822329,0.01309404,0.0001511148,0.0002547072,0.002891256],"study_design_scores_gemma":[0.001354646,0.00003674049,0.00001578881,0.0004488811,0.00001647157,0.00002412419,0.00005286376,0.9941472,0.001794174,0.001828601,0.0000185652,0.0002618878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003030232,0.00003438956,0.9928096,0.0002421659,0.00009533412,0.0005377058,0.000002994389,0.0003264565,0.002921124],"genre_scores_gemma":[0.5314659,0.000005956365,0.4682532,0.00005177077,0.00003151422,0.00005440765,0.000006360997,0.00006838034,0.00006244602],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5284357,"threshold_uncertainty_score":0.9999895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03767410724835893,"score_gpt":0.263141590138535,"score_spread":0.2254674828901761,"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."}}