{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002218872,0.00005555269,0.00005134283,0.00004040563,0.00009210976,0.0001187265,0.00006824639,0.00002414402,0.00002344276],"category_scores_gemma":[0.00004735588,0.00005053536,0.00001109765,0.0001023176,0.00002550134,0.0001945282,0.0000257453,0.00004608398,0.000009865076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001193483,"about_ca_system_score_gemma":0.00001110578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003686032,"about_ca_topic_score_gemma":0.000003027594,"domain_scores_codex":[0.9994975,0.00005945524,0.00009573863,0.0001584335,0.00008326549,0.0001056353],"domain_scores_gemma":[0.999739,0.00005191306,0.00002972534,0.0001215349,0.00002873787,0.00002906819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001056295,0.00007345266,0.0008764282,0.00003814091,0.00002292265,0.00005314327,0.001474502,3.612847e-7,0.5119513,0.01342592,0.1946505,0.2774228],"study_design_scores_gemma":[0.0001917751,0.00004052643,0.0001056135,0.000002527751,0.000001562773,0.00003479186,0.00009044345,0.0002083298,0.9958016,0.001240272,0.002201899,0.00008065808],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02651578,0.0001501629,0.9448187,0.0004753628,0.00007468708,0.0001399531,0.000001147112,0.000449627,0.0273746],"genre_scores_gemma":[0.4914115,0.00002916477,0.5051234,0.001061245,0.00001972142,0.00001454531,0.000001002047,0.000007638693,0.002331809],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4838503,"threshold_uncertainty_score":0.2060772,"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."}}