{"id":"W2108201369","doi":"10.1109/cgiv.2007.49","title":"Improved Co-occurrence Matrix as a Feature Space for Relative Entropy-based Image Thresholding","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Thresholding; Pattern recognition (psychology); Artificial intelligence; Entropy (arrow of time); Co-occurrence matrix; Computer science; Feature (linguistics); Kullback–Leibler divergence; Image (mathematics); Feature vector; Computer vision; Mathematics; Image segmentation; Image texture; Physics","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":[0.001140007,0.0006430047,0.00102228,0.002571111,0.0004757303,0.001193205,0.0008353709,0.0007041372,0.001323627],"category_scores_gemma":[0.004601316,0.0003086604,0.0007861524,0.00217838,0.0007387798,0.001534652,0.0005954136,0.00101837,0.0006274648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004641292,"about_ca_system_score_gemma":0.0004167985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00102198,"about_ca_topic_score_gemma":0.001147985,"domain_scores_codex":[0.998579,0.0003126029,0.0001080891,0.0002756972,0.0006461495,0.00007840918],"domain_scores_gemma":[0.9977869,0.001063484,0.0002804467,0.0002223462,0.0005569747,0.00008983014],"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":[0.000609936,0.0001544652,0.002669896,0.0005203323,0.0001988251,0.000383707,0.0002701825,0.05621914,0.2058486,0.02205578,0.002655886,0.7084133],"study_design_scores_gemma":[0.00001986408,0.000247573,0.00520449,0.00003619334,0.00009152694,0.0008737906,0.00006886476,0.9077027,0.06991614,0.0111679,0.004578512,0.0000924477],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01198553,0.0003958997,0.9866592,0.00006244027,0.00003576128,0.0000323882,0.00004569948,0.000392301,0.0003906247],"genre_scores_gemma":[0.2943225,0.0004701709,0.7034023,0.00005789324,0.0001257975,0.0001453631,0.0003043052,0.000171481,0.001000186],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002571111,"threshold_uncertainty_score":0.00602901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470949016742687,"score_gpt":0.3479556842705924,"score_spread":0.3332461941031656,"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."}}