{"id":"W3161646182","doi":"10.1109/access.2021.3078411","title":"RHN: A Residual Holistic Neural Network for Edge Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Residual; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Edge detection; Computational complexity theory; Deep learning; Artificial neural network; Pattern recognition (psychology); Machine learning; Image processing; Image (mathematics); Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002683498,0.0006409354,0.000552475,0.0005400052,0.000167055,0.0003693082,0.001228719,0.0006925549,0.003171549],"category_scores_gemma":[0.000543415,0.0002763354,0.0004559207,0.000467435,0.000276258,0.0008256917,0.0006825835,0.0007155058,0.001089729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003729686,"about_ca_system_score_gemma":0.0004296209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003790773,"about_ca_topic_score_gemma":0.006305356,"domain_scores_codex":[0.9998713,0.00001283425,0.000005469296,0.00004482259,0.00004592934,0.0000197522],"domain_scores_gemma":[0.9998927,0.00001871818,0.00001436314,0.00002244449,0.00004101203,0.00001076663],"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":[0.000284935,0.0001131106,0.001198275,0.0002088864,0.0001374685,0.0001687816,0.00003719776,0.1633758,0.04146115,0.005564884,0.01395675,0.7734928],"study_design_scores_gemma":[0.00001224694,0.00008190044,0.0005635477,0.00001548494,0.00002472275,0.0001076526,0.000008104123,0.9839045,0.008742739,0.002123965,0.004401289,0.00001368832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03070288,0.00155105,0.955483,0.000209265,0.0002294761,0.00008943955,0.0005657375,0.004470637,0.006698458],"genre_scores_gemma":[0.4609953,0.001308124,0.5125992,0.0006013653,0.0001335279,0.000136886,0.002535003,0.0004751805,0.0212154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003790773,"threshold_uncertainty_score":0.01060992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07776004354620641,"score_gpt":0.3146392750302652,"score_spread":0.2368792314840588,"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."}}