{"id":"W2262900641","doi":"10.2312/egve.20151309","title":"Analysis of Depth Perception with Virtual Mask in Stereoscopic AR","year":2015,"lang":"en","type":"article","venue":"Eurographics","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer vision; Stereoscopy; Augmented reality; Depth perception; Virtual image; Object (grammar); Artificial intelligence; Computer science; Perception; Illusion; Transparency (behavior); Computer graphics (images); Virtual reality; Overlay; Object detection; Segmentation","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":[0.000251302,0.0002463716,0.000179977,0.0005359928,0.0001280037,0.0003727008,0.0003004923,0.0002482031,0.001452112],"category_scores_gemma":[0.001288541,0.0001613592,0.000281161,0.000327679,0.0003379219,0.0005880363,0.0003202051,0.0002839716,0.0001108811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003888471,"about_ca_system_score_gemma":0.0002337553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00188909,"about_ca_topic_score_gemma":0.001107043,"domain_scores_codex":[0.9997106,0.00003958087,0.0000063633,0.00003792107,0.0001615123,0.00004399881],"domain_scores_gemma":[0.9995301,0.0002173211,0.00007162664,0.00004371718,0.0001125922,0.00002471298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001210628,0.0001187448,0.004424495,0.0003958372,0.00005534067,0.0003224828,0.0007296707,0.03851409,0.8136236,0.01248921,0.0006317984,0.1274841],"study_design_scores_gemma":[0.00005695466,0.0005305649,0.06436381,0.00003780836,0.00006196927,0.0009770873,0.000369489,0.7661707,0.1602117,0.004658452,0.002475687,0.00008584001],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7376905,0.00062241,0.2565506,0.00008424046,0.00002301456,0.00006930065,0.000183361,0.0001973712,0.004579116],"genre_scores_gemma":[0.9784605,0.0002192821,0.02072899,0.00001839553,0.000007553534,0.00001235661,0.0000617792,0.00002336347,0.0004677835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00188909,"threshold_uncertainty_score":0.004857838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0344211665358749,"score_gpt":0.2762364717922621,"score_spread":0.2418153052563872,"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."}}