{"id":"W1594874717","doi":"10.1007/11559573_17","title":"Edge Detection Models","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Piecewise; Constant (computer programming); Laplace operator; Linear approximation; Contrast (vision); Piecewise linear function; Computer science; Approximation algorithm; Image (mathematics); Edge detection; Algorithm; Segmentation; Image segmentation; Enhanced Data Rates for GSM Evolution; Applied mathematics; Artificial intelligence; Mathematics; Image processing; Mathematical analysis; Nonlinear system; 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":[0.0004078862,0.001340929,0.0008688106,0.001337136,0.0004550111,0.001908463,0.002298534,0.001771945,0.01686337],"category_scores_gemma":[0.001620841,0.000730862,0.001144559,0.001425886,0.0004064245,0.002250685,0.0008078025,0.001422592,0.01583643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005090158,"about_ca_system_score_gemma":0.0004633017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003010853,"about_ca_topic_score_gemma":0.003090263,"domain_scores_codex":[0.9996786,0.00002906216,0.00001059631,0.0001382945,0.0001079869,0.00003547419],"domain_scores_gemma":[0.9995732,0.00009767168,0.00003322088,0.0001297166,0.0001471415,0.0000189772],"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":[0.0001776424,0.0001846583,0.001018458,0.0002866095,0.000106803,0.0001762436,0.00007909832,0.1952172,0.01751064,0.1008399,0.03867371,0.6457291],"study_design_scores_gemma":[0.00001069685,0.00004831189,0.0005312737,0.00004390164,0.00004782651,0.0002329817,0.00001837504,0.9156349,0.009893916,0.03941387,0.03409448,0.00002950977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005396348,0.001619793,0.9656243,0.000302844,0.0002661598,0.00007653868,0.000715171,0.002508255,0.02349061],"genre_scores_gemma":[0.2776222,0.006143294,0.4927654,0.0006178861,0.0004092275,0.0003449375,0.007775715,0.001663131,0.2126582],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01686337,"threshold_uncertainty_score":0.05641359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02259402804802146,"score_gpt":0.2675673996897302,"score_spread":0.2449733716417087,"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."}}