{"id":"W2023008685","doi":"10.1109/icip.2012.6466850","title":"Bilateral filter based mixture model for image segmentation","year":2012,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Mixture model; Artificial intelligence; Markov random field; Image segmentation; Expectation–maximization algorithm; Computer science; Pattern recognition (psychology); Pixel; Segmentation; Noise (video); Bilateral filter; Filter (signal processing); Computer vision; Image (mathematics); Mathematics; Maximum likelihood; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001404354,0.0009711481,0.001550795,0.001600189,0.000554472,0.001250293,0.001861617,0.002016472,0.004113093],"category_scores_gemma":[0.002605967,0.0007901423,0.002148387,0.00191195,0.0006872008,0.002530993,0.0009951338,0.001535348,0.001854282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001295874,"about_ca_system_score_gemma":0.0009328497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007182943,"about_ca_topic_score_gemma":0.006792154,"domain_scores_codex":[0.9990022,0.0002147033,0.00005072651,0.0002308323,0.0004245538,0.00007707919],"domain_scores_gemma":[0.9994517,0.0002755524,0.00005508663,0.00006538274,0.0001289638,0.00002337877],"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":[0.0001769441,0.00007137319,0.0007370539,0.0002495968,0.0001847784,0.0001557368,0.0001742222,0.6115013,0.02630337,0.05534501,0.004202501,0.3008982],"study_design_scores_gemma":[0.000005070763,0.00001316766,0.0001712371,0.000008077795,0.00001843103,0.00006527935,0.000005719891,0.9850574,0.001993304,0.00932344,0.003320606,0.00001823756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006046852,0.000163801,0.9984509,0.00003755537,0.00001762579,0.00001325098,0.0000317089,0.0002906219,0.0003898913],"genre_scores_gemma":[0.1167153,0.001416163,0.8725473,0.0001970716,0.0001377337,0.0002928779,0.0006598384,0.0004766502,0.00755702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007182943,"threshold_uncertainty_score":0.01428229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02599157995916512,"score_gpt":0.2519902157476298,"score_spread":0.2259986357884646,"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."}}