{"id":"W1994347084","doi":"10.1109/igarss.2008.4780100","title":"Segmentation of Polarimetric SAR Data based on the Fisher Distribution for Texture Modeling","year":2008,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Wishart distribution; Pattern recognition (psychology); Artificial intelligence; Synthetic aperture radar; Computer science; Covariance matrix; Cluster analysis; Image segmentation; Segmentation; Image texture; Inverse-Wishart distribution; Covariance; Computer vision; Algorithm; Mathematics; Machine learning; Statistics","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":[0.0001200048,0.00007422059,0.00007355023,0.00003823238,0.00007195675,0.000006700208,0.0001820565,0.00005399689,0.00004241119],"category_scores_gemma":[0.00004095232,0.0000501008,0.00002894001,0.000192153,0.00001463616,0.0000545245,0.00001403333,0.00005670344,0.000001860989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003129629,"about_ca_system_score_gemma":0.00001027432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004156455,"about_ca_topic_score_gemma":0.00000216986,"domain_scores_codex":[0.9995654,0.000007572331,0.0001331325,0.0001121567,0.0001012139,0.0000805825],"domain_scores_gemma":[0.9993507,0.000143049,0.00002030585,0.0004363972,0.00003470792,0.0000148633],"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":[0.00006092267,0.0004027472,0.0009251466,0.0001902849,0.0001502889,8.271951e-7,0.0002331759,0.01418899,0.005685854,0.02169306,0.1562522,0.8002165],"study_design_scores_gemma":[0.00008256681,0.0000146216,0.00008422987,0.000007454919,0.00001225269,9.731694e-7,0.00001724598,0.9454341,0.01263805,0.0002712834,0.04137571,0.00006153986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002763657,0.00006656138,0.9948778,0.0003227953,0.00002018474,0.0003489371,0.0002463676,0.0001444499,0.001209248],"genre_scores_gemma":[0.8442135,0.00002087994,0.1550474,0.00007533569,0.00002575917,0.000009875119,0.0005692265,0.00001307368,0.00002486432],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9312451,"threshold_uncertainty_score":0.2043051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04463377001264916,"score_gpt":0.252198558045722,"score_spread":0.2075647880330729,"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."}}