{"id":"W2023739648","doi":"10.1109/icices.2014.7033989","title":"Fabric quality testing using image processing","year":2014,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alpha Technologies (Canada)","funders":"","keywords":"Weaving; Textile; Artificial intelligence; Process (computing); Computer vision; Computer science; Woven fabric; Clothing; Texture (cosmology); Gabor filter; Visual inspection; Image processing; Feature extraction; Image (mathematics); Engineering; Materials science; Mechanical engineering; Composite material","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004874908,0.00008698194,0.0001282812,0.00005823314,0.00009869023,0.00008524923,0.00004024563,0.0000692828,0.00002340241],"category_scores_gemma":[0.0002410019,0.0000769066,0.0000284963,0.0002652061,0.000008498477,0.0001606827,0.00001106972,0.0001021989,0.00004051348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004400602,"about_ca_system_score_gemma":0.000008759696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001645625,"about_ca_topic_score_gemma":0.000003566544,"domain_scores_codex":[0.9993459,0.00003907954,0.0002340131,0.0001066306,0.0001158966,0.0001584754],"domain_scores_gemma":[0.9996784,0.0000790009,0.0000347269,0.0001063911,0.00005967488,0.00004182286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004736383,0.00001036011,0.00146647,0.0002367526,0.00001001666,0.000001329543,0.0001299915,0.01711486,0.6242754,0.0001265929,0.0003248974,0.3562986],"study_design_scores_gemma":[0.0002870722,0.00002327994,0.001817247,0.00007711972,0.000007398004,0.00001917201,0.0001052358,0.9654957,0.029888,0.0001425188,0.001882158,0.0002550677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5761142,0.00001811741,0.3717244,0.000002624064,0.0003260591,0.00007582747,4.104259e-7,0.0006138286,0.05112455],"genre_scores_gemma":[0.9895042,1.116415e-7,0.01004328,0.00001264001,0.0003391768,0.000002175096,2.647541e-7,0.00001942028,0.00007878456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9483809,"threshold_uncertainty_score":0.313616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0761009398184522,"score_gpt":0.2995944848890914,"score_spread":0.2234935450706392,"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."}}