{"id":"W1985351004","doi":"10.1515/jisys.2011.015","title":"Automatic Detection of Defects on Periodically Patterned Textures","year":2011,"lang":"en","type":"article","venue":"Journal of Intelligent Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"University of Hong Kong","keywords":"Histogram; Block (permutation group theory); Pattern recognition (psychology); Block size; Artificial intelligence; Matrix (chemical analysis); Distortion (music); Cluster analysis; Mathematics; Similarity (geometry); Hierarchical clustering; Luminance; Computer science; Measure (data warehouse); Image quality; Contrast (vision); Image (mathematics); Computer vision; Key (lock); Data mining; Combinatorics","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":[0.0002325045,0.0002313229,0.0003583884,0.001200511,0.0001262659,0.0003436948,0.0003333887,0.0002588473,0.0005569095],"category_scores_gemma":[0.001175503,0.0001452282,0.0002067434,0.0003801202,0.0001994108,0.0002455914,0.0002578611,0.0002010329,0.0002074435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001626053,"about_ca_system_score_gemma":0.0001783565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000945157,"about_ca_topic_score_gemma":0.0009612747,"domain_scores_codex":[0.9997014,0.00004442239,0.00001155426,0.00005986992,0.0001507171,0.00003193592],"domain_scores_gemma":[0.9990907,0.0002810336,0.0001759611,0.0001276933,0.0002791446,0.00004558188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005752549,0.000101349,0.01337089,0.0001430752,0.00005113891,0.0002903476,0.00009731021,0.01443176,0.5951951,0.0006742365,0.001139137,0.3739303],"study_design_scores_gemma":[0.00003798518,0.000300282,0.04829751,0.00001642844,0.00004219552,0.000825134,0.00008591122,0.6829417,0.2651888,0.0007985701,0.001431355,0.00003421088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7598413,0.0001856467,0.2366667,0.00005018255,0.00003064823,0.00004695616,0.000135224,0.001825477,0.001217975],"genre_scores_gemma":[0.9129226,0.00006596724,0.08623729,0.00001160666,0.000009386559,0.00001437669,0.0001627415,0.00004834195,0.0005278621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001200511,"threshold_uncertainty_score":0.001879275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03334697812977805,"score_gpt":0.2253316849668286,"score_spread":0.1919847068370505,"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."}}