{"id":"W2098922885","doi":"10.1109/cadvis.1994.284510","title":"A robust method for registration and segmentation of multiple range images","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada","keywords":"Artificial intelligence; Outlier; Computer science; Segmentation; Thresholding; Computer vision; Range (aeronautics); RANSAC; Pixel; Image segmentation; Noise (video); Estimator; Image registration; Pattern recognition (psychology); Image (mathematics); Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006034145,0.00004111719,0.0000574564,0.00002847232,0.00001621497,0.00001092999,0.00001244068,0.00002481401,0.00002935044],"category_scores_gemma":[0.00002252361,0.00003953629,0.0000140314,0.00003941675,0.000006158168,0.00006107934,0.00000144237,0.00001255678,6.065254e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007710376,"about_ca_system_score_gemma":6.54526e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001676539,"about_ca_topic_score_gemma":0.00001846794,"domain_scores_codex":[0.9997457,0.00000756747,0.0001040404,0.00005432214,0.00004096858,0.00004735882],"domain_scores_gemma":[0.9998349,0.00005955912,0.00001707721,0.00004568023,0.00002866415,0.00001410464],"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":[0.000006177749,0.00001686477,0.00057309,0.0001600019,0.00001508586,1.965227e-7,0.0002160825,0.9072391,0.07135368,0.0007849386,0.003106279,0.01652848],"study_design_scores_gemma":[0.0003680238,0.00002054723,0.0003000604,0.000004811839,0.000009235012,6.460194e-7,0.00005252605,0.9516327,0.04741831,0.00004865574,0.0001004039,0.00004408623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005093852,0.00009226774,0.9936042,0.00005693717,0.00003123056,0.0001599485,0.000006278748,0.00004299046,0.0009123001],"genre_scores_gemma":[0.5842434,0.00007115599,0.4153598,0.0000162803,0.00001935549,0.00001055964,0.0000191991,0.00001041023,0.0002498575],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5791495,"threshold_uncertainty_score":0.1612243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03974928067419743,"score_gpt":0.2440420840950708,"score_spread":0.2042928034208734,"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."}}