{"id":"W1824514060","doi":"10.1109/icassp.1995.480069","title":"A segmentation criterion for digital image compression","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Lossy compression; Image segmentation; Color Cell Compression; Block (permutation group theory); Pixel; Segmentation; Pattern recognition (psychology); Image compression; Computer science; Scale-space segmentation; Range segmentation; Mathematics; Measure (data warehouse); Segmentation-based object categorization; Computer vision; Smoothness; Image (mathematics); Image processing; Data mining","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":[0.00007003255,0.00006988937,0.00006750483,0.00005726409,0.00006570054,0.0003374648,0.0002790803,0.00002560722,0.0003005785],"category_scores_gemma":[0.00004867782,0.00005831643,0.00003719426,0.0001028956,0.00002771802,0.001575876,0.00008305605,0.00003389682,0.00008675721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002254495,"about_ca_system_score_gemma":0.000003323236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001729075,"about_ca_topic_score_gemma":1.345243e-7,"domain_scores_codex":[0.9993029,0.00001483108,0.0001620463,0.0002011653,0.0001894631,0.0001295925],"domain_scores_gemma":[0.9995575,0.00006654375,0.00004584784,0.0001962335,0.00006416329,0.00006969992],"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":[0.000002587035,0.0001067622,0.00002671988,0.00002366595,0.000003957489,0.000003003491,0.000421674,3.496488e-7,0.1128936,0.001274396,0.0931214,0.7921219],"study_design_scores_gemma":[0.0009952204,0.0002666736,0.0001274148,0.00004240481,0.000004168637,0.00001479444,0.00008039677,0.267998,0.7223856,0.003771421,0.004032077,0.0002817571],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003895635,0.00001769708,0.9935467,0.0007760529,0.00009762257,0.0003033663,0.000003640041,0.0004482912,0.004417112],"genre_scores_gemma":[0.04987156,0.00001013528,0.9470645,0.00103685,0.00003727744,0.00008024616,0.0000160603,0.000006758409,0.001876575],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7918401,"threshold_uncertainty_score":0.3291124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02849586564272617,"score_gpt":0.2967979571833998,"score_spread":0.2683020915406736,"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."}}