{"id":"W2128035609","doi":"10.1109/ccece.2008.4564491","title":"Mean shift point-mass level-of-detail","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Counterintuitive; Point (geometry); Metric (unit); Computer science; Segmentation; Measure (data warehouse); Image (mathematics); Image segmentation; Field (mathematics); Artificial intelligence; Homogeneous; Pattern recognition (psychology); Algorithm; Mathematics; Data mining; Geometry; Physics; Combinatorics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0008288832,0.0007160041,0.0007504245,0.002028029,0.0003846113,0.001440424,0.0009355079,0.000846096,0.001975753],"category_scores_gemma":[0.002951211,0.000344308,0.0007663443,0.00124089,0.0006166353,0.001635775,0.00105342,0.0007374173,0.0009331821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009586694,"about_ca_system_score_gemma":0.0004882954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00167827,"about_ca_topic_score_gemma":0.001907714,"domain_scores_codex":[0.9992035,0.000072537,0.00003285255,0.0001496381,0.0004833258,0.00005815431],"domain_scores_gemma":[0.9989924,0.0002759045,0.0001655357,0.0001761525,0.0003383836,0.00005168518],"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":[0.0006173597,0.0001151434,0.009145638,0.0004604353,0.0003004645,0.0002753369,0.0003427229,0.128893,0.1559326,0.03017878,0.004823146,0.6689153],"study_design_scores_gemma":[0.00004158763,0.0006394809,0.02301183,0.00007129955,0.0001910067,0.001203332,0.0001419271,0.8266432,0.1067601,0.02268172,0.01844718,0.0001674026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03706092,0.0008954364,0.9584756,0.0001448761,0.00007085183,0.00007004255,0.000143584,0.0008088352,0.002329851],"genre_scores_gemma":[0.5143225,0.0009524883,0.4798849,0.0001261769,0.0001510869,0.0000917225,0.0003978359,0.0003114358,0.003761896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002028029,"threshold_uncertainty_score":0.006955624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03186372917017686,"score_gpt":0.2054821520585913,"score_spread":0.1736184228884144,"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."}}