{"id":"W2131686127","doi":"10.1109/icme.2009.5202578","title":"A new localized superpixel Markov random field for image segmentation","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Markov random field; Artificial intelligence; Pixel; Computer science; Segmentation; Pattern recognition (psychology); Image segmentation; Markov process; Markov chain; Conditional random field; Process (computing); Computer vision; Random field; Field (mathematics); Image (mathematics); Machine learning; Mathematics; Statistics","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":[0.0001431849,0.0001035902,0.0001400078,0.00005541676,0.00006392954,0.0001269934,0.0003422975,0.00004334441,0.00006538152],"category_scores_gemma":[0.0001056848,0.00008535976,0.00008573704,0.0001856872,0.000007117331,0.0008773087,0.00003772861,0.00005234584,0.00001094342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001990627,"about_ca_system_score_gemma":0.00003832799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001837751,"about_ca_topic_score_gemma":0.000001853372,"domain_scores_codex":[0.9992439,0.00001813136,0.0001761689,0.0002438097,0.000125321,0.0001926342],"domain_scores_gemma":[0.9993851,0.000162706,0.0000366756,0.0002731949,0.00006566357,0.00007673039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001354803,0.00002247038,0.000007187125,0.000004971114,0.000004360223,0.000003796883,0.00007682151,5.571547e-7,0.02901155,0.008779417,0.04735116,0.9146022],"study_design_scores_gemma":[0.003102896,0.0005926674,0.00004254288,0.00001430106,0.000007155013,0.000009840311,0.00001547586,0.006313049,0.8920307,0.07816298,0.01949351,0.0002148832],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00006060273,0.00008345261,0.9917596,0.003179727,0.00006499072,0.0004581303,6.953999e-7,0.0004020314,0.003990748],"genre_scores_gemma":[0.008210482,0.00005079389,0.9848515,0.004122851,0.00006608245,0.00001572136,0.000003498314,0.00000474787,0.002674317],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9143873,"threshold_uncertainty_score":0.348087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0108879387097444,"score_gpt":0.3052329758931945,"score_spread":0.2943450371834501,"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."}}