{"id":"W2141502150","doi":"10.1109/icassp.2008.4517764","title":"Fast automated stopping-time and edge-strength estimation for anisotropic diffusion","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Smoothing; Anisotropic diffusion; Enhanced Data Rates for GSM Evolution; Estimator; Noise (video); Computer science; Algorithm; Iterative and incremental development; Edge detection; Diffusion; Scale space; Stopping time; Iterative method; Process (computing); Mathematical optimization; Computer vision; Mathematics; Image processing; Image (mathematics); Statistics; Physics","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":[0.0001982069,0.0001748244,0.0001948528,0.0001437489,0.000285958,0.0002374144,0.000565237,0.00008049088,0.00001368508],"category_scores_gemma":[0.0001888144,0.0001366724,0.00003835492,0.0001285478,0.0003076629,0.0006228373,0.0001467624,0.0001537643,0.000001561728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004785239,"about_ca_system_score_gemma":0.00008711471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006334059,"about_ca_topic_score_gemma":1.784992e-7,"domain_scores_codex":[0.9986276,0.00000838615,0.0003469662,0.0003481928,0.0004946087,0.0001742201],"domain_scores_gemma":[0.9986545,0.00007390855,0.0003602679,0.00006885606,0.0007583333,0.00008407656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006798187,0.000186869,0.0004047551,0.0002888848,0.00003326409,0.000002560415,0.001453366,0.0000763492,0.7419901,0.004754871,0.00157121,0.2491698],"study_design_scores_gemma":[0.0004181016,0.0001426719,0.0006968167,0.0003571998,0.00001488087,0.00004210371,0.00009347723,0.9077045,0.08658236,0.003789393,0.000008364996,0.0001501162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2037525,0.00002576517,0.7936955,0.0008637382,0.0001720093,0.0003585208,0.00001467172,0.0003141815,0.0008031374],"genre_scores_gemma":[0.8431438,0.00006388116,0.1561795,0.0001974651,0.00007396559,0.00001523351,0.00000397911,0.00001040814,0.0003118333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9076282,"threshold_uncertainty_score":0.5573339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03129967091420923,"score_gpt":0.2915892096156246,"score_spread":0.2602895387014154,"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."}}