{"id":"W3119306060","doi":"10.47893/ijipvs.2012.1004","title":"Similarity Measures for Automatic Defect Detection on Patterned Textures","year":2012,"lang":"en","type":"article","venue":"International Journal of Image Processing and Vision Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"University of Hong Kong","keywords":"Jaccard index; Similarity (geometry); Bhattacharyya distance; Pattern recognition (psychology); Histogram; Artificial intelligence; Pearson product-moment correlation coefficient; Correlation coefficient; Mathematics; Computer science; Similarity measure; Euclidean distance; Image (mathematics); Statistics","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":[0.001649837,0.00009397408,0.00012662,0.0003476234,0.0001707206,0.0003330745,0.0001774314,0.00005029865,0.00000431264],"category_scores_gemma":[0.0004864327,0.00006935043,0.00007326445,0.0001698475,0.00007396573,0.001046884,0.00001837338,0.0001520588,0.000002560109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009476244,"about_ca_system_score_gemma":0.00003429261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002564469,"about_ca_topic_score_gemma":7.184911e-7,"domain_scores_codex":[0.998781,0.00002303521,0.0003135071,0.00009558749,0.0006242943,0.00016257],"domain_scores_gemma":[0.9990134,0.00009315043,0.0001715196,0.00005365157,0.0005674299,0.000100855],"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":[0.00005316585,0.00002918262,0.0001922071,0.00003082731,0.00001418443,0.000001315104,0.000274501,0.0001822628,0.1435495,0.000008699728,0.0001383776,0.8555257],"study_design_scores_gemma":[0.002245258,0.0008089656,0.01969506,0.001094556,0.00005900767,0.0007033006,0.000450304,0.1943997,0.7726845,0.0007003927,0.006702125,0.0004568374],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7299979,0.0003931827,0.2668984,0.00008786241,0.00223629,0.00009499506,0.000003075817,0.00005094154,0.0002373223],"genre_scores_gemma":[0.9979645,0.000012129,0.001443345,0.00004919007,0.0005121555,0.000003158327,1.952037e-7,0.000009031382,0.000006321721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8550689,"threshold_uncertainty_score":0.3211845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02457320365587629,"score_gpt":0.3493450594806942,"score_spread":0.3247718558248179,"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."}}