{"id":"W4288075324","doi":"10.18280/ts.390315","title":"An Improved BM3D-Canny-Zernike Algorithm for Micro-Size Detection of Electronic Connectors","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Zernike polynomials; Canny edge detector; Subpixel rendering; Algorithm; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Pixel; Computer science; Edge detection; Mathematics; Mean squared error; Computer vision; Pattern recognition (psychology); Image processing; Image (mathematics); Statistics; Optics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001015832,0.0009388635,0.0009283553,0.002875656,0.0003931192,0.0007014915,0.00120642,0.00106114,0.001931652],"category_scores_gemma":[0.001818441,0.0004989271,0.0008086013,0.001865501,0.0004072112,0.001499975,0.0005722411,0.0006991649,0.001080901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007186165,"about_ca_system_score_gemma":0.001059007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003654816,"about_ca_topic_score_gemma":0.005067328,"domain_scores_codex":[0.9985452,0.0001340251,0.00006757137,0.000240677,0.000935042,0.00007754547],"domain_scores_gemma":[0.9992263,0.0001038892,0.00007193546,0.00009196212,0.0004708391,0.00003497599],"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":[0.000272744,0.00009983875,0.001294234,0.0002113574,0.0000820866,0.00009955277,0.00006726781,0.01680192,0.2096903,0.004190223,0.003473429,0.7637171],"study_design_scores_gemma":[0.00008102227,0.0003005489,0.00648062,0.00002555445,0.00009892721,0.000996572,0.00003476214,0.7645459,0.2081703,0.002017636,0.01711403,0.0001341635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01555129,0.0007105981,0.9808668,0.00008797363,0.00007820471,0.0000623132,0.00005628702,0.001401334,0.001185249],"genre_scores_gemma":[0.09726813,0.0005834105,0.8988035,0.0001010311,0.00005829413,0.00007742135,0.000222064,0.000140437,0.002745697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003654816,"threshold_uncertainty_score":0.007267058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00851818156788188,"score_gpt":0.2131485052161576,"score_spread":0.2046303236482757,"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."}}