{"id":"W2124112726","doi":"10.1109/pacrim.1995.519600","title":"Unsupervised range image segmentation for rapid prototyping","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Scanner; Computer vision; Computer science; Artificial intelligence; Segmentation; Merge (version control); Image segmentation; Range segmentation; Laser scanning; Market segmentation; Scale-space segmentation; Region growing; Range (aeronautics); Computer graphics (images); Laser; Engineering; Optics; Information retrieval","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001426446,0.0000731119,0.00006960987,0.00008669028,0.0001147709,0.0001380436,0.0002311895,0.00002939657,0.000222604],"category_scores_gemma":[0.00001691799,0.00006474882,0.00005576139,0.0001807356,0.00001208377,0.0007334543,0.00003303858,0.000038319,0.00006125478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002490948,"about_ca_system_score_gemma":0.000004765688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008328755,"about_ca_topic_score_gemma":0.000001252752,"domain_scores_codex":[0.9994144,0.00002273647,0.000124644,0.000193557,0.00009410274,0.0001505424],"domain_scores_gemma":[0.9996176,0.00002972407,0.00002979442,0.0002181175,0.00007541744,0.0000293796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005233936,0.00004039342,0.000007279975,0.00003062161,0.000006062063,0.000001824426,0.0004825775,1.597834e-7,0.02563172,0.0009236616,0.007860503,0.96501],"study_design_scores_gemma":[0.0004812666,0.000158794,0.00003718735,0.0000068966,0.000002581834,0.000006116829,0.00002803162,0.04041722,0.9450124,0.00116442,0.01255186,0.0001332138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002186568,0.00008157096,0.9881127,0.0005691862,0.0000784848,0.001064575,6.871743e-7,0.0006911919,0.009182965],"genre_scores_gemma":[0.04285588,0.0000733557,0.9522194,0.001496073,0.00009827108,0.00106723,0.000001876839,0.00001442183,0.002173545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9648768,"threshold_uncertainty_score":0.264038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02456192665659683,"score_gpt":0.2520290677471408,"score_spread":0.227467141090544,"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."}}