{"id":"W2156066310","doi":"10.1109/icnn.1995.488998","title":"Enhanced Hopfield network for morphologic image processing","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Hopfield network; Computer science; Artificial neural network; Flexibility (engineering); Artificial intelligence; Feature extraction; Pattern recognition (psychology); Simplicity; Image processing; Content-addressable memory; Filter (signal processing); Image (mathematics); Feature (linguistics); Associative property; Computer vision; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002307281,0.0003062568,0.0002679029,0.0003407489,0.0002243875,0.0004344723,0.0005495973,0.0005542714,0.003966638],"category_scores_gemma":[0.0006191325,0.0001044151,0.0002726691,0.0004258168,0.0003174825,0.0009093427,0.0003331015,0.0004203542,0.0007454871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004413518,"about_ca_system_score_gemma":0.0003852182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001446433,"about_ca_topic_score_gemma":0.002279121,"domain_scores_codex":[0.9998939,0.00001605437,0.00000584472,0.00002398432,0.00005013173,0.00001003084],"domain_scores_gemma":[0.9999026,0.00002905938,0.000007784042,0.0000145395,0.00003788577,0.000008076721],"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":[0.0002524504,0.00006620662,0.0005918393,0.0002447146,0.00006122307,0.0005943721,0.000143395,0.1686876,0.1736484,0.1241008,0.005221004,0.526388],"study_design_scores_gemma":[0.00002879185,0.00008715129,0.0005015397,0.00002152493,0.0000323779,0.0004679189,0.000020482,0.9006953,0.03662289,0.03974973,0.02174199,0.00003027956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01269127,0.000726467,0.9793745,0.0001374593,0.00006700897,0.00003406209,0.00006549474,0.0004711962,0.006432619],"genre_scores_gemma":[0.3820367,0.001264308,0.6018954,0.0001469868,0.00008158101,0.00008048298,0.0001730342,0.00006676341,0.01425471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003966638,"threshold_uncertainty_score":0.01326972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0294986180111601,"score_gpt":0.2595679097121899,"score_spread":0.2300692917010298,"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."}}