{"id":"W2170391517","doi":"10.1109/83.846240","title":"KCS-new kernel family with compact support in scale space: formulation and impact","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Kernel (algebra); Gaussian; Gaussian function; Scale space; Computer science; Representation (politics); Gaussian process; Mathematics; Algorithm; Kernel method; Pattern recognition (psychology); Artificial intelligence; Image processing; Support vector machine; Discrete mathematics; 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":[0.0001905495,0.000194714,0.0001929583,0.0002458763,0.0001588854,0.0003487868,0.0002190336,0.00006409562,0.0001847621],"category_scores_gemma":[0.000002552951,0.0001616865,0.0000409708,0.00059437,0.00008080024,0.002202155,0.00000131691,0.0002647005,0.00002525257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001064032,"about_ca_system_score_gemma":0.0002236021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002281604,"about_ca_topic_score_gemma":0.00004387713,"domain_scores_codex":[0.9986583,0.0000408402,0.0002662889,0.0003795417,0.0003602831,0.0002947508],"domain_scores_gemma":[0.9993956,0.00004126792,0.00007165015,0.0002329858,0.00005685219,0.0002016497],"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":[0.00007397577,0.0001599034,0.0002512429,0.00003876376,0.000008986421,0.00001976618,0.001541776,0.0009809739,0.01613254,0.000002005705,0.0002826805,0.9805074],"study_design_scores_gemma":[0.005240179,0.001493566,0.03241828,0.0008273769,0.00007028572,0.0003390232,0.0003585855,0.3347592,0.6216573,0.001296868,0.000181546,0.001357803],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05440063,0.00003557905,0.9436371,0.0003364921,0.0000271129,0.0002398456,0.000002921015,0.0002776306,0.001042704],"genre_scores_gemma":[0.8367856,0.00003430742,0.1624082,0.0002588497,0.00001631056,0.00001082987,0.000001902594,0.00001704676,0.0004669521],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9791496,"threshold_uncertainty_score":0.6593385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01408051740961766,"score_gpt":0.2946639673317581,"score_spread":0.2805834499221405,"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."}}