{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004715659,0.0006841909,0.0006463618,0.001224104,0.0005070473,0.0008499697,0.0009362738,0.0007069289,0.007067312],"category_scores_gemma":[0.00156094,0.000671434,0.0006094502,0.0007940415,0.0004799405,0.0009164538,0.0009277601,0.0007939803,0.002817477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003171191,"about_ca_system_score_gemma":0.0004920203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005946899,"about_ca_topic_score_gemma":0.0009365197,"domain_scores_codex":[0.9991184,0.000170047,0.00003772353,0.0001718699,0.0004278538,0.00007414273],"domain_scores_gemma":[0.9988507,0.0004777132,0.00008757135,0.00030825,0.0002353935,0.00004036137],"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":[0.0001494995,0.00005045289,0.0004495924,0.0002470509,0.0000428575,0.0002101025,0.0001642002,0.03190453,0.2750406,0.01248178,0.00599654,0.6732628],"study_design_scores_gemma":[0.00005123092,0.0002285415,0.002396154,0.0000605904,0.00004920527,0.001609857,0.0001119272,0.6263669,0.2884226,0.02251885,0.05808739,0.00009670811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002739625,0.00009081494,0.9937784,0.00002604981,0.0000114918,0.00004169632,0.00004531488,0.002102798,0.001163729],"genre_scores_gemma":[0.05072912,0.0001551952,0.9457728,0.0000284155,0.0000239225,0.0001749421,0.0002619184,0.000556039,0.002297701],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007067312,"threshold_uncertainty_score":0.02364254,"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."}}