{"id":"W2171857727","doi":"10.1109/cvpr.1992.223208","title":"Range image segmentation and fitting by residual consensus","year":2003,"lang":"en","type":"article","venue":"","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Research Council Canada","keywords":"Residual; Histogram; Range (aeronautics); Computation; Image segmentation; Image processing; Sample (material); Computer science; Set (abstract data type); Segmentation; Artificial intelligence; Image (mathematics); Noise (video); Algorithm; Surface (topology); Mathematics; Pattern recognition (psychology); Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001381865,0.001049441,0.001885431,0.003090364,0.0007121667,0.001678203,0.002566245,0.002262098,0.004820476],"category_scores_gemma":[0.005264871,0.001114551,0.00166784,0.002311946,0.00101595,0.002212486,0.00173311,0.001306322,0.003000287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001034623,"about_ca_system_score_gemma":0.0007920562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0037107,"about_ca_topic_score_gemma":0.003683015,"domain_scores_codex":[0.998041,0.0002758229,0.0001119552,0.0006726971,0.000775292,0.0001233445],"domain_scores_gemma":[0.9978689,0.0005382556,0.0001814345,0.0006886403,0.000664335,0.00005839888],"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":[0.0003181366,0.000140059,0.001118371,0.0001674578,0.0002039514,0.0001686066,0.0004548113,0.2537012,0.07989987,0.02855789,0.00450087,0.6307687],"study_design_scores_gemma":[0.00002436911,0.00008069529,0.0004113496,0.00001057561,0.0000327654,0.0001427658,0.00004962862,0.9474143,0.03651405,0.01063035,0.004637225,0.00005183777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005603625,0.00006226972,0.9916295,0.00003600267,0.00002553646,0.00005748844,0.00002687272,0.00121317,0.001345506],"genre_scores_gemma":[0.08424796,0.00008360909,0.9095474,0.00006400196,0.00002971518,0.0001213936,0.0002255229,0.0006389,0.005041539],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004820476,"threshold_uncertainty_score":0.01612604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006874596049944498,"score_gpt":0.2372157901970207,"score_spread":0.2303411941470762,"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."}}