{"id":"W2130160942","doi":"10.1109/icip.2007.4379515","title":"Edge Sensitive Variational Image Thresholding","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Energy functional; Minimax; Regularization (linguistics); Computer science; Thresholding; Energy minimization; Variational method; Weighting; Artificial intelligence; Smoothing; Image segmentation; Energy (signal processing); Fidelity; Image (mathematics); Algorithm; Mathematical optimization; Mathematics; Computer vision","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":[0.0008319684,0.0004915168,0.0008876375,0.0008215323,0.0003505335,0.001050541,0.001985379,0.001466732,0.002289797],"category_scores_gemma":[0.002077759,0.0005879074,0.0007678318,0.000646252,0.001095179,0.001225637,0.001199404,0.001496769,0.0007346873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006288161,"about_ca_system_score_gemma":0.000581344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009828283,"about_ca_topic_score_gemma":0.001002569,"domain_scores_codex":[0.999496,0.0001012284,0.00002344921,0.0001175575,0.0002242052,0.00003755967],"domain_scores_gemma":[0.9995781,0.0001856572,0.00004853056,0.0000807965,0.00007997538,0.00002699152],"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":[0.00009849756,0.00006127558,0.0007652709,0.0003463245,0.0001344667,0.0002478865,0.0002987577,0.3782237,0.09907683,0.2316631,0.004480934,0.2846029],"study_design_scores_gemma":[0.000008046884,0.00003001144,0.0001770142,0.00001493882,0.00001445995,0.0002006676,0.00001365443,0.9566787,0.01077187,0.02742757,0.004641707,0.00002118952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001898457,0.0001256125,0.9970818,0.00006818636,0.00001717198,0.00001246332,0.00001161717,0.0001055454,0.000679102],"genre_scores_gemma":[0.1614071,0.0004998595,0.8313603,0.000282987,0.00008187271,0.0001085663,0.0001276463,0.0003771317,0.005754604],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002289797,"threshold_uncertainty_score":0.007660151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01528684749122643,"score_gpt":0.3000587533706988,"score_spread":0.2847719058794724,"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."}}