{"id":"W2831321715","doi":"10.1109/access.2018.2852663","title":"Automatic Visual Defect Detection Using Texture Prior and Low-Rank Representation","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National University of Defense Technology; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Computer vision; Rank (graph theory); Visual inspection; Pattern recognition (psychology); Texture (cosmology); Representation (politics); Image texture; Process (computing); Image (mathematics); Feature extraction; Image processing; 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":[0.0004695274,0.0006351713,0.0007378159,0.001630813,0.0001879626,0.0008737269,0.0007707668,0.0006877413,0.0009889129],"category_scores_gemma":[0.001976567,0.000303642,0.0007152566,0.0007174805,0.0005881769,0.001321336,0.0008174703,0.0008325462,0.0006401892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002955629,"about_ca_system_score_gemma":0.0005095809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001651942,"about_ca_topic_score_gemma":0.00196737,"domain_scores_codex":[0.9994618,0.00008232205,0.00002026683,0.0001242778,0.0002454279,0.0000658535],"domain_scores_gemma":[0.9989755,0.0002651724,0.0002082971,0.0002133634,0.0002892062,0.00004840828],"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":[0.00031685,0.0002398105,0.001989793,0.0002423757,0.00006791532,0.0001961738,0.0001339366,0.1010825,0.293745,0.00703113,0.002541218,0.5924133],"study_design_scores_gemma":[0.00001212791,0.00008355016,0.001707133,0.000008719579,0.00001751176,0.0001922412,0.00002310327,0.9631215,0.03118353,0.002718551,0.0009097958,0.00002223624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0286808,0.0001022106,0.9698992,0.00007115965,0.0000131272,0.00002815325,0.00005220609,0.0006645672,0.0004884166],"genre_scores_gemma":[0.4663951,0.0003910276,0.5294906,0.00009843325,0.00006879178,0.0000788749,0.0005572557,0.0002086179,0.002711331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001651942,"threshold_uncertainty_score":0.003308177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03361194343589494,"score_gpt":0.3292761522339615,"score_spread":0.2956642087980665,"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."}}