{"id":"W2152078447","doi":"10.1109/igarss.2001.977103","title":"Rapid determination of co-occurrence texture features","year":2002,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Scheme for Promotion of Academic and Research Collaboration","keywords":"Grey level; Computer science; Co-occurrence matrix; Artificial intelligence; Pattern recognition (psychology); Image (mathematics); Texture (cosmology); Co-occurrence; Matrix (chemical analysis); Computational complexity theory; Table (database); Algorithm; Image processing; Image texture; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003135764,0.0003760718,0.0005845911,0.003730352,0.0003353821,0.0009275621,0.0004995952,0.0004487883,0.00314388],"category_scores_gemma":[0.002297352,0.0002458181,0.0003259006,0.002499138,0.0003094867,0.001183691,0.0006540053,0.0005867179,0.00172619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002768222,"about_ca_system_score_gemma":0.000501083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001755059,"about_ca_topic_score_gemma":0.002838729,"domain_scores_codex":[0.9995946,0.00003251236,0.00001535023,0.00006575925,0.0002238393,0.00006790643],"domain_scores_gemma":[0.998538,0.0004686464,0.0001555522,0.0001535846,0.0006138188,0.00007027279],"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":[0.0004957426,0.00009404532,0.009258322,0.0002918044,0.00005828902,0.0003936344,0.0001735588,0.008984545,0.3105747,0.004004568,0.006936021,0.6587349],"study_design_scores_gemma":[0.00005493816,0.0002468261,0.05964568,0.00004546531,0.00009137419,0.00182489,0.0004921689,0.617954,0.2939624,0.008737545,0.01681772,0.0001270091],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1800736,0.0006136531,0.808717,0.0001828931,0.0001030448,0.0001533939,0.001151082,0.003630412,0.005374885],"genre_scores_gemma":[0.5773224,0.0003915845,0.4174135,0.00005684758,0.00008761846,0.0001084176,0.001567048,0.0002887531,0.002763922],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003730352,"threshold_uncertainty_score":0.0105173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02898712871475154,"score_gpt":0.2735974626842249,"score_spread":0.2446103339694733,"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."}}