{"id":"W1515861206","doi":"10.1007/978-3-642-15702-8_16","title":"A Complete Visual Hull Representation Using Bounding Edges","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Hull; Visual hull; Bounding overwatch; Computer science; Representation (politics); Enhanced Data Rates for GSM Evolution; Algorithm; Artificial intelligence; Minimum bounding box; Computer vision; Image (mathematics); Iterative reconstruction","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":[0.0002350498,0.001094124,0.001265993,0.001338204,0.0003646095,0.002370918,0.00170683,0.001118297,0.009623094],"category_scores_gemma":[0.000718321,0.0007569001,0.00105661,0.00172825,0.0004646254,0.002387913,0.002009099,0.001629591,0.00421507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002811974,"about_ca_system_score_gemma":0.0004837847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002233684,"about_ca_topic_score_gemma":0.002174098,"domain_scores_codex":[0.9996026,0.00003111199,0.000020423,0.00008589051,0.0002193917,0.00004059952],"domain_scores_gemma":[0.9997793,0.00003006706,0.00001928635,0.00007996577,0.00007262395,0.00001880641],"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":[0.0001676535,0.0000875527,0.0001664224,0.0003454424,0.00004838118,0.0002294477,0.0001367839,0.09761298,0.03563446,0.05813522,0.02118122,0.7862544],"study_design_scores_gemma":[0.0000288333,0.00009169515,0.0003045986,0.00008850927,0.00003694337,0.000461401,0.00009702097,0.9051116,0.01408146,0.04773112,0.03191904,0.0000479165],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002966107,0.000138266,0.9925129,0.00004778086,0.0000371717,0.00003636599,0.0003398985,0.00094033,0.002981225],"genre_scores_gemma":[0.1067894,0.0008462294,0.8745388,0.0001029869,0.00007161983,0.0001275615,0.002481273,0.0007421016,0.01429995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009623094,"threshold_uncertainty_score":0.03219241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04624486198316703,"score_gpt":0.3371638518815994,"score_spread":0.2909189898984324,"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."}}