{"id":"W1969427705","doi":"10.1117/12.484104","title":"Preprocessing of Edge of Light images: towards a quantitative evaluation","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Rivet; Enhanced Data Rates for GSM Evolution; Brightness; Fuselage; Edge detection; Feature extraction; Computer science; Preprocessor; Corrosion; Artificial intelligence; Computer vision; Structural engineering; Engineering; Image processing; Materials science; Image (mathematics); Optics; Physics","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":[0.01180694,0.0008736295,0.00127476,0.003885567,0.0012457,0.004591538,0.001657424,0.001947284,0.01104112],"category_scores_gemma":[0.04140088,0.0005010453,0.0006983864,0.002424496,0.0009589256,0.003624529,0.001254723,0.0007442644,0.007966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005397815,"about_ca_system_score_gemma":0.001929789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009276863,"about_ca_topic_score_gemma":0.001820078,"domain_scores_codex":[0.9867764,0.003441685,0.001134382,0.0009433245,0.00733349,0.0003707583],"domain_scores_gemma":[0.9307508,0.01492191,0.004110683,0.008586802,0.04026288,0.001366839],"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":[0.001134801,0.0004658466,0.01651802,0.001421363,0.0001965368,0.0002519399,0.0001950327,0.003557455,0.1606092,0.002808418,0.02248452,0.7903568],"study_design_scores_gemma":[0.0002550936,0.002946566,0.1091408,0.0005028302,0.00064812,0.002646849,0.001020673,0.1637586,0.6017554,0.00968165,0.1073895,0.0002538985],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1840731,0.004376693,0.7608538,0.002896265,0.001495389,0.001759806,0.003362677,0.01129273,0.02988956],"genre_scores_gemma":[0.4738637,0.002486536,0.4798462,0.0005906383,0.0008811642,0.000595541,0.008292144,0.002795462,0.03064857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01180694,"threshold_uncertainty_score":0.06244189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02603109404327978,"score_gpt":0.2704535111091204,"score_spread":0.2444224170658406,"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."}}