{"id":"W2020513142","doi":"10.1109/lsp.2007.913625","title":"A New Model for Image Segmentation","year":2008,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Image segmentation; Segmentation; Computer science; Scale-space segmentation; Artificial intelligence; Segmentation-based object categorization; Image (mathematics); Computer vision; Set (abstract data type); Pattern recognition (psychology); Variable (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001584033,0.0001332528,0.0001213212,0.0001052136,0.0002141915,0.000147679,0.0004632271,0.00003757156,0.00001128432],"category_scores_gemma":[0.0000141684,0.0001300506,0.00005772819,0.0002071873,0.00007191289,0.001250048,0.00002935491,0.00009826038,0.00001409515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006058081,"about_ca_system_score_gemma":0.0001932023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008572201,"about_ca_topic_score_gemma":3.30775e-7,"domain_scores_codex":[0.9987766,0.00002275012,0.0002406086,0.0003381202,0.0003651048,0.0002568358],"domain_scores_gemma":[0.9994594,0.0000449643,0.0001240394,0.0001587033,0.00007966299,0.0001332697],"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":[0.000007459884,0.00001945695,0.000006052436,0.00004009576,0.000005192779,0.00001493641,0.001500386,0.0006859695,0.7370626,0.00002099549,0.06976569,0.1908712],"study_design_scores_gemma":[0.0003847535,0.0000295612,0.000008420112,0.0000277055,0.000005214479,0.00002467843,0.000006996576,0.6164803,0.3820459,0.0007975657,0.00003063901,0.0001582282],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00160838,0.0000257725,0.9949747,0.002561983,0.0000709328,0.0002789451,0.000001432511,0.0004148821,0.0000629139],"genre_scores_gemma":[0.06710316,0.000002794639,0.923253,0.009145204,0.0001253038,0.00005854288,0.00000462263,0.0000155945,0.0002917552],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6157944,"threshold_uncertainty_score":0.5303308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03619138375877132,"score_gpt":0.2935405548139096,"score_spread":0.2573491710551383,"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."}}