{"id":"W2159224728","doi":"10.1109/pacrim.2009.5291293","title":"An optimization method for edge-detector parameter tuning based on visual perception","year":2009,"lang":"en","type":"article","venue":"","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Sobel operator; Canny edge detector; Deriche edge detector; Detector; Edge detection; Blob detection; Artificial intelligence; Computer vision; Computer science; Enhanced Data Rates for GSM Evolution; Parameterized complexity; Image gradient; Gaussian; Mathematics; Algorithm; Image (mathematics); Image processing; 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":[0.001407088,0.001164199,0.0009465678,0.0008813026,0.000393194,0.0007968689,0.001338827,0.001069666,0.002153346],"category_scores_gemma":[0.003419297,0.0007398729,0.0006280108,0.0006878534,0.0006930557,0.001025501,0.0007655115,0.001148874,0.0008115345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007744599,"about_ca_system_score_gemma":0.001255732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002277624,"about_ca_topic_score_gemma":0.002136345,"domain_scores_codex":[0.999329,0.0001874882,0.00003357889,0.0001497963,0.0002634714,0.00003669061],"domain_scores_gemma":[0.9990945,0.0003808176,0.0001065053,0.00007108828,0.0003173057,0.00002986996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000132147,0.0001377907,0.0005056376,0.000221296,0.0001139262,0.00007468041,0.0001161019,0.5166636,0.05962386,0.01617446,0.003295507,0.402941],"study_design_scores_gemma":[0.00001512828,0.00003942898,0.0001716487,0.000008737717,0.00001374122,0.0000415447,0.000006155496,0.9908449,0.00500972,0.002305734,0.001520217,0.00002306103],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006394078,0.00003344546,0.9989554,0.00001363593,0.000005655533,0.00001265023,0.000003871922,0.0001384286,0.0001975002],"genre_scores_gemma":[0.05437975,0.0001078292,0.9441049,0.00004389255,0.00002404011,0.0001874578,0.0000411633,0.0002244753,0.0008864464],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002277624,"threshold_uncertainty_score":0.007441461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01754604146523982,"score_gpt":0.3479898426325976,"score_spread":0.3304438011673578,"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."}}