{"id":"W2106097225","doi":"10.1109/tpami.2005.173","title":"Canny edge detection enhancement by scale multiplication","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":601,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Multiplication (music); Edge detection; Thresholding; Scale (ratio); Canny edge detector; Mathematics; Enhanced Data Rates for GSM Evolution; Scalar multiplication; Pattern recognition (psychology); Artificial intelligence; Deriche edge detector; Computer science; Image processing; Image (mathematics)","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":[0.00007508345,0.0001468374,0.0001417358,0.0001882017,0.0001534715,0.00005081681,0.0001019691,0.00005361064,0.0001274698],"category_scores_gemma":[5.666332e-7,0.0001453406,0.00008798063,0.000448818,0.0000314174,0.0001092837,0.00000106373,0.0001635904,0.00003426448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006500004,"about_ca_system_score_gemma":0.000003164444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003242531,"about_ca_topic_score_gemma":0.001231449,"domain_scores_codex":[0.9992609,0.00001101347,0.0002396978,0.000237463,0.0001052622,0.0001456344],"domain_scores_gemma":[0.9996217,0.00001762705,0.00003525024,0.0002208121,0.00003803541,0.00006655407],"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":[0.000002090411,0.00006944811,0.00002353605,0.00001148839,0.00008319544,6.568373e-8,0.00008302378,0.01708519,0.03591273,7.209361e-7,0.00004433021,0.9466842],"study_design_scores_gemma":[0.00002402246,0.00001505179,0.00004248904,0.000005680015,0.0001355686,0.000001107943,0.000009333322,0.3265966,0.6718632,0.00001363671,0.001182132,0.0001111961],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008256849,0.0002059199,0.9907988,0.0001767627,0.00003545559,0.0001238358,0.00004262595,0.0002287339,0.0001309627],"genre_scores_gemma":[0.9964421,0.0007124293,0.002282386,0.0001177784,0.00002171826,0.0001521364,0.0000138938,0.00001672192,0.0002408787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9885164,"threshold_uncertainty_score":0.5926816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009654834140319035,"score_gpt":0.2536779626350946,"score_spread":0.2440231284947756,"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."}}