{"id":"W2126520869","doi":"10.1109/icip.2008.4711949","title":"Robust snake convergence based on dynamic programming","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Convergence (economics); Hidden Markov model; Boundary (topology); Computer vision; Viterbi algorithm; Algorithm; Dynamic programming; Object (grammar); Noise (video); Active contour model; Pattern recognition (psychology); Image segmentation; Image (mathematics); Mathematics","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":[0.002366542,0.0007874153,0.001773964,0.001270333,0.0006210094,0.001336731,0.001800599,0.001424728,0.00276323],"category_scores_gemma":[0.007687938,0.00100829,0.001140861,0.0007758149,0.001542877,0.00143349,0.003199042,0.002272119,0.001095654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008548907,"about_ca_system_score_gemma":0.001001246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001163425,"about_ca_topic_score_gemma":0.0007545951,"domain_scores_codex":[0.9990275,0.0002702289,0.00004429049,0.0001800434,0.0004135005,0.00006444136],"domain_scores_gemma":[0.9976623,0.001356183,0.0001774219,0.000222712,0.0004755882,0.0001057457],"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":[0.0001097892,0.00004589275,0.0004470791,0.0001263307,0.00006124636,0.0001118847,0.0001798108,0.776793,0.009747622,0.09508707,0.001870656,0.1154196],"study_design_scores_gemma":[0.00000487925,0.00001286367,0.0000358774,0.000005348728,0.000002530993,0.00002218602,0.000003809437,0.9894429,0.0007929906,0.009134493,0.000535802,0.000006286807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003059135,0.00008122469,0.9954745,0.00005476735,0.000009269745,0.00001592213,0.000006501344,0.0002696301,0.00102907],"genre_scores_gemma":[0.2452077,0.0003701143,0.7480553,0.0001217217,0.00004253819,0.0002880666,0.0001010609,0.0005913646,0.00522223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00276323,"threshold_uncertainty_score":0.0125156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03585852864350347,"score_gpt":0.2701679945955919,"score_spread":0.2343094659520884,"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."}}