{"id":"W2157481672","doi":"10.1109/icip.2008.4711823","title":"Image segmentation using histogram specification","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Scale-space segmentation; Histogram; Image segmentation; Artificial intelligence; Computer science; Segmentation-based object categorization; Segmentation; Computer vision; Region growing; Image histogram; Histogram matching; Pattern recognition (psychology); Image texture; Image (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":[0.0001264564,0.00006897236,0.00006543097,0.00009381961,0.0001189176,0.00005021422,0.0003020348,0.00002662509,0.0002039861],"category_scores_gemma":[0.0000249658,0.00006513375,0.0000287769,0.000270928,0.00007160416,0.000850417,0.00005676427,0.00005365206,0.0001026753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009905175,"about_ca_system_score_gemma":0.0000386905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000041034,"about_ca_topic_score_gemma":6.924019e-7,"domain_scores_codex":[0.999149,0.00004105226,0.0001925874,0.0002146858,0.0002804094,0.0001222705],"domain_scores_gemma":[0.999461,0.00002278654,0.00007323157,0.0002928612,0.00007918348,0.00007096121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002296338,0.0001279251,0.0005104606,0.00001026407,0.000007048343,0.00003247573,0.001048885,0.000003819583,0.8404531,0.003795746,0.01697851,0.1370295],"study_design_scores_gemma":[0.0004308499,0.00007202804,0.003378917,0.00001052332,0.000005385431,0.0001584026,0.00009393266,0.09397026,0.8990076,0.001291191,0.001260346,0.0003206325],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004827312,0.00001550221,0.9896055,0.0002024344,0.0001112029,0.0001566514,2.635988e-7,0.0005132681,0.004567827],"genre_scores_gemma":[0.02341876,0.00002603211,0.9755822,0.0004715229,0.00003616557,0.00001080819,0.00000533773,0.000005240033,0.0004438843],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1367088,"threshold_uncertainty_score":0.2656077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05787486013757925,"score_gpt":0.313251656833109,"score_spread":0.2553767966955298,"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."}}