{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006903865,0.0008462096,0.0009823809,0.001176619,0.0005039628,0.002095239,0.002168378,0.00239701,0.00381064],"category_scores_gemma":[0.001707199,0.0005925207,0.001490944,0.001423858,0.001204992,0.002929545,0.001694831,0.002027427,0.002401642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001211067,"about_ca_system_score_gemma":0.001020308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002174623,"about_ca_topic_score_gemma":0.002160935,"domain_scores_codex":[0.9991574,0.000148547,0.00004505771,0.0002357157,0.0003478991,0.00006531383],"domain_scores_gemma":[0.9995546,0.0001388957,0.00004636322,0.00009941497,0.0001209508,0.00003975869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001107813,0.00005201839,0.0005197783,0.0002751564,0.00008655072,0.0003671188,0.000266531,0.3552846,0.02361763,0.4812393,0.01423835,0.1239422],"study_design_scores_gemma":[0.00001100764,0.0000279909,0.0001030187,0.00001756306,0.00001683342,0.000215138,0.00001506669,0.9116366,0.001577151,0.06328583,0.02307209,0.00002167992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000983073,0.0003313756,0.9955834,0.000258065,0.00009930134,0.00002134208,0.00008662621,0.00024307,0.002393865],"genre_scores_gemma":[0.1395105,0.002628037,0.8270183,0.0008113448,0.0006674802,0.0005308128,0.001012175,0.0005938209,0.02722741],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00381064,"threshold_uncertainty_score":0.01274788,"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."}}