{"id":"W2131026578","doi":"10.1109/ccece.2004.1349767","title":"A hybrid algorithm using discrete cosine transform and Gabor filter bank for texture segmentation","year":2004,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Filter bank; Discrete cosine transform; Gabor filter; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Computer science; Gabor transform; Filter (signal processing); Computer vision; Feature vector; Segmentation; Feature (linguistics); Image texture; Mathematics; Algorithm; Image segmentation; Time–frequency analysis; Image (mathematics)","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":[0.0007901204,0.0007895364,0.001369085,0.002226657,0.000497363,0.001215295,0.001193174,0.00135031,0.00323885],"category_scores_gemma":[0.001129094,0.0004801844,0.0007945229,0.001802627,0.0005287533,0.001343466,0.0006104084,0.0006418111,0.001826862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007482369,"about_ca_system_score_gemma":0.0008159042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004593031,"about_ca_topic_score_gemma":0.005192661,"domain_scores_codex":[0.9993236,0.00006114687,0.00004522088,0.0001963639,0.0002897477,0.00008387833],"domain_scores_gemma":[0.9995661,0.0001150629,0.00003268859,0.00006092698,0.000193661,0.00003162077],"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":[0.0003736324,0.0001266616,0.0007815075,0.00009564246,0.00009606945,0.0001014964,0.00005446519,0.02347374,0.1094716,0.00546203,0.00354802,0.8564151],"study_design_scores_gemma":[0.00008749988,0.0002320592,0.001756153,0.00001627224,0.00007954876,0.0005446175,0.00004224307,0.9291621,0.05552627,0.003005031,0.009499866,0.0000483589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006965014,0.0001931803,0.9907126,0.00005088008,0.00007153999,0.00006584825,0.00002491236,0.000956522,0.0009595421],"genre_scores_gemma":[0.05825407,0.0001905531,0.9378355,0.00009981434,0.0000562142,0.0001301061,0.000144637,0.0001139222,0.003175247],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004593031,"threshold_uncertainty_score":0.01083505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01766764663950859,"score_gpt":0.2779444969890839,"score_spread":0.2602768503495753,"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."}}