{"id":"W4311165016","doi":"10.18280/ts.390536","title":"Threshold Values of Different Classical Edge Detection Algorithms","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Prewitt operator; Sobel operator; Digital image; Edge detection; Canny edge detector; Algorithm; Threshold limit value; Image (mathematics); Mathematics; Enhanced Data Rates for GSM Evolution; Range (aeronautics); Computer science; Noise (video); Artificial intelligence; Image processing; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002506265,0.0001354594,0.0002048913,0.0001314392,0.0001516539,0.00001859049,0.00009417472,0.0000554617,0.0006389506],"category_scores_gemma":[0.000003227782,0.0001251145,0.0001212513,0.0001777983,0.00001788199,0.00005734755,0.00003976974,0.0002395037,0.000007773977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000150915,"about_ca_system_score_gemma":0.000007931409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001076301,"about_ca_topic_score_gemma":0.000003340518,"domain_scores_codex":[0.9988402,0.00006198399,0.0003491172,0.0001503705,0.0004177365,0.0001806198],"domain_scores_gemma":[0.9997168,0.00003536874,0.00005416022,0.0001155586,0.00002391819,0.00005422374],"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":[0.0003138434,0.0005162557,0.0009386296,0.0001637126,0.0003359927,0.00002002762,0.001279783,0.1030077,0.6677554,0.000782409,0.007170514,0.2177157],"study_design_scores_gemma":[0.003304731,0.002132947,0.006272436,0.00004904253,0.0001059888,0.0000491061,0.0009666581,0.5054145,0.4594465,0.00062361,0.02096752,0.0006669433],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743926,0.00008889161,0.02206786,0.0000171995,0.00155631,0.0003311855,0.00002677156,0.0002252832,0.001293916],"genre_scores_gemma":[0.999355,0.000003300044,0.00002087781,0.00001036402,0.0003946928,0.00009413442,0.000007701015,0.00002277524,0.00009115223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4024068,"threshold_uncertainty_score":0.6996061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02240242423770257,"score_gpt":0.2254854954882277,"score_spread":0.2030830712505252,"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."}}