{"id":"W4301805958","doi":"","title":"Image Segmentation Through Efficient Boundary Sampling","year":2009,"lang":"en","type":"preprint","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Image segmentation; Computer vision; Artificial intelligence; Scale-space segmentation; Segmentation; Boundary (topology); Segmentation-based object categorization; 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":[0.0000892274,0.000220986,0.0001686675,0.00005309809,0.0001062681,0.0002640531,0.0002165952,0.0001557588,0.00006959914],"category_scores_gemma":[0.000004484497,0.0002285688,0.00007652298,0.00009503608,0.00003265829,0.00006576686,0.000123225,0.0003938889,0.00004652161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001337066,"about_ca_system_score_gemma":0.00003507491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001612354,"about_ca_topic_score_gemma":0.000001011505,"domain_scores_codex":[0.9991407,0.000006579809,0.0002542339,0.0002613516,0.0001366857,0.0002004625],"domain_scores_gemma":[0.9994996,0.00001216927,0.00004656776,0.000354886,0.00005581275,0.00003092218],"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":[0.00001154027,0.0003806781,0.00001271195,0.003213265,0.0001703418,0.00001240044,0.002608955,0.4518753,0.1793147,0.008393418,0.06872648,0.2852803],"study_design_scores_gemma":[0.0002730891,0.00002374071,0.0001477247,0.0005073167,0.0001092323,0.00001456708,0.0001311908,0.7208308,0.1353199,0.109457,0.03180326,0.001382202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002757188,0.0006647239,0.9575987,0.000172532,0.0001501744,0.0003555592,0.00001880238,0.002373814,0.03590851],"genre_scores_gemma":[0.04999984,0.0002212939,0.9490346,0.0001442744,0.0001321549,0.0001489477,0.0001764328,0.00004894258,0.00009349528],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2838981,"threshold_uncertainty_score":0.9320765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0243061529455425,"score_gpt":0.3082628519956519,"score_spread":0.2839566990501094,"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."}}