{"id":"W2069780241","doi":"10.1117/12.583105","title":"Smoothing depth maps for improved steroscopic image quality","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer vision; Artificial intelligence; Rendering (computer graphics); Smoothing; Stereoscopy; Gaussian blur; Computer science; Depth map; Image-based modeling and rendering; Image quality; Bilateral filter; View synthesis; Computer graphics (images); Image restoration; Image (mathematics); Image processing","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":[0.0007320226,0.0005779262,0.0003614954,0.0006350583,0.0001804121,0.0005687315,0.0003478306,0.0003482704,0.003046854],"category_scores_gemma":[0.006043648,0.0002507465,0.0003456766,0.0003893907,0.0002607885,0.0005808751,0.0005377911,0.0006142658,0.0002644017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002169131,"about_ca_system_score_gemma":0.0002415149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001064403,"about_ca_topic_score_gemma":0.001139341,"domain_scores_codex":[0.9995955,0.00008492019,0.0000227104,0.00004766081,0.0002129578,0.00003628645],"domain_scores_gemma":[0.9977922,0.001239148,0.0002443522,0.0002606489,0.0003964231,0.00006722885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004986051,0.00009635013,0.001518466,0.0003426233,0.00003792845,0.0001527167,0.000522809,0.005686006,0.8622649,0.001390976,0.0003927435,0.1270958],"study_design_scores_gemma":[0.0002287193,0.002653571,0.06784932,0.000113458,0.000290624,0.002168362,0.0004247898,0.1236047,0.7860368,0.005215337,0.01124211,0.0001721991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5206311,0.0009896368,0.4738695,0.0001898968,0.00005348349,0.000189106,0.0001411157,0.001535961,0.002400112],"genre_scores_gemma":[0.7766578,0.0005866052,0.221223,0.00006219634,0.00002217937,0.00006810202,0.0001080487,0.0001781599,0.001093906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003046854,"threshold_uncertainty_score":0.01019269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02108333110236295,"score_gpt":0.2861542561169953,"score_spread":0.2650709250146324,"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."}}