{"id":"W1539882801","doi":"10.1109/ijcnn.1992.227305","title":"Multiresolution edge detection","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Edge detection; Computer science; Artificial intelligence; Representation (politics); Scale (ratio); Position (finance); Range (aeronautics); Artificial neural network; Computer vision; Image (mathematics); Detector; Pattern recognition (psychology); 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":[0.0004684482,0.0006676367,0.000837595,0.002000881,0.0003217063,0.0009399353,0.0009056187,0.0008568452,0.006948078],"category_scores_gemma":[0.001457178,0.0003527801,0.0005922122,0.001591809,0.0002147249,0.001269366,0.0006414121,0.0005493294,0.003126321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005083709,"about_ca_system_score_gemma":0.0002546349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001279636,"about_ca_topic_score_gemma":0.001829976,"domain_scores_codex":[0.9995448,0.0000417929,0.00002422795,0.0001027708,0.0002458878,0.00004045117],"domain_scores_gemma":[0.9996357,0.00008725173,0.00004846467,0.0000529425,0.0001601988,0.0000154517],"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":[0.0001792835,0.00005072504,0.0006282525,0.000457328,0.00006340401,0.000215656,0.00003787561,0.01594448,0.04800653,0.007356439,0.006556404,0.9205036],"study_design_scores_gemma":[0.00005296678,0.0004203611,0.008246775,0.000511015,0.000292184,0.005165323,0.0001261793,0.6085606,0.1971302,0.02143571,0.1578961,0.0001625908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01093059,0.01330525,0.9618658,0.00022903,0.0002272815,0.00009538753,0.0002623441,0.002082161,0.0110021],"genre_scores_gemma":[0.1385765,0.01269156,0.8269821,0.0002847919,0.0001526911,0.0001257538,0.000778011,0.0004112855,0.01999732],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006948078,"threshold_uncertainty_score":0.02324367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01598769001768295,"score_gpt":0.2684181272422167,"score_spread":0.2524304372245337,"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."}}