{"id":"W2131077461","doi":"10.1109/ccece.2002.1013070","title":"A Markov random fields model for hybrid edge- and region-based color image segmentation","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Image segmentation; Artificial intelligence; Computer science; Markov random field; Computer vision; Scale-space segmentation; Segmentation; Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Region growing; Markov chain; Markov process; Edge detection; Image (mathematics); Segmentation-based object categorization; Image processing; 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":[0.0003336784,0.00010119,0.0001249979,0.00007091583,0.00008941905,0.0001236581,0.0001864809,0.00003735277,0.00003165955],"category_scores_gemma":[0.0002010528,0.00008808962,0.00004426729,0.00008993811,0.00005609868,0.0003787409,0.00002788628,0.00005485817,0.000002858281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002718516,"about_ca_system_score_gemma":0.00008360672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005739987,"about_ca_topic_score_gemma":0.000002493545,"domain_scores_codex":[0.9991184,0.00006447625,0.0002033888,0.0002826661,0.0001645494,0.0001665],"domain_scores_gemma":[0.9992325,0.0002754837,0.00006548747,0.0002302409,0.00009586956,0.0001003858],"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":[0.0004991924,0.000606134,0.0002145981,0.0004544739,0.00008351985,0.00006853268,0.001631347,0.0007767605,0.05006054,0.03943432,0.4527978,0.4533727],"study_design_scores_gemma":[0.002058227,0.00006739383,0.000003303677,0.000007958164,0.000006024915,0.000006660004,0.00001521993,0.7730896,0.2201184,0.004436734,0.00008638953,0.000104117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008156255,0.00002041143,0.9957753,0.00109445,0.00006732972,0.0006892011,0.000002025098,0.0002146057,0.00132098],"genre_scores_gemma":[0.0767916,0.00001211216,0.9178647,0.003838402,0.00001050688,0.0002338418,0.000006618162,0.000007233661,0.001234983],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7723128,"threshold_uncertainty_score":0.359219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01907827385034751,"score_gpt":0.278920321444098,"score_spread":0.2598420475937505,"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."}}