{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001826472,0.0008117015,0.001135649,0.001213215,0.0004676438,0.001099406,0.001523072,0.001205637,0.0007900124],"category_scores_gemma":[0.00798097,0.000683086,0.0006579533,0.0008956737,0.0007849789,0.001245301,0.00125798,0.001239049,0.0004982896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006973174,"about_ca_system_score_gemma":0.001031172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001108217,"about_ca_topic_score_gemma":0.001613908,"domain_scores_codex":[0.9987047,0.0003277613,0.000132345,0.000195794,0.0005852479,0.00005411437],"domain_scores_gemma":[0.9958981,0.001986479,0.0005667865,0.0004733055,0.0009410416,0.0001342619],"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":[0.0003926565,0.0001010285,0.002327648,0.0003629595,0.0001181423,0.0002173955,0.0002756136,0.2107836,0.160923,0.02373915,0.002895534,0.5978633],"study_design_scores_gemma":[0.0000217897,0.00003473212,0.0003105371,0.000008001716,0.00001220789,0.000121547,0.000008129958,0.9710435,0.02250248,0.004028672,0.001874888,0.000033397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002914328,0.00008111818,0.996663,0.00002487628,0.00001133899,0.000007703974,0.000007494164,0.0001720736,0.0001180623],"genre_scores_gemma":[0.06393171,0.0001467118,0.9350321,0.00003220686,0.00002841788,0.00005384081,0.0000761303,0.0001405483,0.0005583222],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001826472,"threshold_uncertainty_score":0.00965941,"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."}}