{"id":"W1501826617","doi":"10.1109/pacrim.1991.160808","title":"Inhomogeneity test for unsupervised texture segmentation","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Texture (cosmology); Artificial intelligence; Image texture; Segmentation; Pattern recognition (psychology); Computer science; A priori and a posteriori; Uncorrelated; Image segmentation; Multivariate statistics; Computer vision; Mathematics; Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004419801,0.0008623034,0.001302501,0.003873823,0.001042483,0.001522392,0.00246155,0.001828664,0.004673014],"category_scores_gemma":[0.02618597,0.0004776569,0.001372108,0.002197303,0.002525525,0.001785676,0.001717847,0.002029893,0.001488422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001087124,"about_ca_system_score_gemma":0.001575613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002202747,"about_ca_topic_score_gemma":0.001936595,"domain_scores_codex":[0.9956293,0.001544852,0.0002216044,0.0006679677,0.001693954,0.0002423941],"domain_scores_gemma":[0.9833589,0.01113334,0.001364969,0.001946513,0.001856964,0.0003393081],"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":[0.001865298,0.0003221521,0.01644649,0.0005237602,0.0007330987,0.0011444,0.0002979489,0.1616461,0.06622845,0.1141009,0.01266654,0.6240248],"study_design_scores_gemma":[0.0001388963,0.0003532671,0.01157929,0.00004689032,0.0001072295,0.0009697744,0.00009280771,0.8665163,0.04617183,0.06232424,0.01156739,0.0001321188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008761453,0.0001271009,0.9882082,0.0001257805,0.00004214404,0.0001011987,0.0001595035,0.001046917,0.001427621],"genre_scores_gemma":[0.2735004,0.0001887834,0.7198012,0.0003395372,0.0002551359,0.0007018707,0.001265361,0.0008451229,0.003102501],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004673014,"threshold_uncertainty_score":0.02337444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03190129939014009,"score_gpt":0.2773783561644177,"score_spread":0.2454770567742777,"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."}}