{"id":"W4231631659","doi":"10.1109/icpr.2004.1334208","title":"Texture segmentation comparison using grey level co-occurrence probabilities and Markov random fields","year":2004,"lang":"en","type":"article","venue":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Texture (cosmology); Consistency (knowledge bases); Segmentation; Feature (linguistics); Image segmentation; Confusion; Markov chain; Computer science; Boundary (topology); Random field; Image texture; Window (computing); Markov random field; Markov process; Mathematics; Gaussian; Grey level; Statistics; Image (mathematics); Psychology; 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":[0.002884321,0.0005471819,0.0009552053,0.004608922,0.0003691275,0.001815061,0.0004011185,0.0009787575,0.001277131],"category_scores_gemma":[0.0108713,0.0003706799,0.0008430745,0.002085898,0.0007637969,0.001670645,0.0005375373,0.0006045218,0.0002642872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000719183,"about_ca_system_score_gemma":0.0005830931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002765438,"about_ca_topic_score_gemma":0.002479769,"domain_scores_codex":[0.9989334,0.0002949935,0.00006572151,0.0001808003,0.0003937677,0.0001313409],"domain_scores_gemma":[0.9947612,0.003760841,0.0004611304,0.0002390738,0.0005970399,0.0001806782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005370404,0.0002137246,0.02103481,0.000667566,0.0003994742,0.0007614372,0.000642516,0.2361681,0.180608,0.01260175,0.00204081,0.5394914],"study_design_scores_gemma":[0.0000396723,0.0003175087,0.02046054,0.00003139558,0.00009287675,0.0004342264,0.0001322431,0.9384405,0.0343543,0.004838589,0.0007815792,0.00007655712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4476109,0.001156224,0.5459796,0.0002572413,0.00009956114,0.0001455469,0.0002296155,0.001333819,0.003187622],"genre_scores_gemma":[0.8910111,0.0004306189,0.1073971,0.00003978605,0.00004731916,0.00004454747,0.0002860427,0.0001333121,0.0006102301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004608922,"threshold_uncertainty_score":0.01525396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09093570772019158,"score_gpt":0.3348014223830179,"score_spread":0.2438657146628263,"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."}}