{"id":"W3023907044","doi":"10.1007/s00138-002-0083-0","title":"Range image segmentation using local approximation of scan lines with application to CAD model acquisition","year":2003,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service","keywords":"Artificial intelligence; CAD; Robustness (evolution); Computer vision; Computer science; Segmentation; Classification of discontinuities; Pixel; Range (aeronautics); Image segmentation; Noise (video); Geometric primitive; Range segmentation; Scale-space segmentation; Pattern recognition (psychology); Image (mathematics); Mathematics; Engineering; Engineering drawing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001079104,0.0001353306,0.0001350157,0.0001433382,0.0001102613,0.00003122913,0.00004796361,0.00005329611,0.000007098961],"category_scores_gemma":[0.00000361819,0.0001216509,0.00002133562,0.0003801181,0.00003823902,0.0001273091,0.000008734726,0.00005375285,0.000004753073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005009299,"about_ca_system_score_gemma":0.00001314672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002830187,"about_ca_topic_score_gemma":0.00001755113,"domain_scores_codex":[0.9992756,0.00001994932,0.0002494872,0.0001933866,0.0001508939,0.0001107298],"domain_scores_gemma":[0.9995452,0.00001679132,0.0000604848,0.0001912,0.0001072997,0.0000790566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001001782,0.00004319908,0.000190592,0.00006977788,0.000006415813,5.500512e-8,0.00008491921,0.8781226,0.0986952,0.006095692,0.00002248118,0.01665911],"study_design_scores_gemma":[0.0003423519,0.00003313003,0.000297785,0.00002093005,0.00002762061,0.000003436572,0.00006825129,0.9770827,0.02129207,0.0005208396,0.0001736079,0.0001372957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06281482,0.00007050009,0.9358405,0.00005271893,0.000009671457,0.0006876998,0.00002999523,0.00008595467,0.0004081572],"genre_scores_gemma":[0.9176259,0.00002965935,0.08196032,0.00004954451,0.00001872747,0.0001289127,0.0001476167,0.00002789088,0.00001141341],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8548111,"threshold_uncertainty_score":0.496078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006973354389244341,"score_gpt":0.2515791495777225,"score_spread":0.2446057951884782,"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."}}