{"id":"W2150444871","doi":"10.1109/icip.2004.1421742","title":"Stereoscopic image generation based on depth images","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Image warping; Computer vision; Artificial intelligence; Computer science; View synthesis; Stereoscopy; Rendering (computer graphics); Image-based modeling and rendering; Smoothing; Depth map; Distortion (music); Virtual image; Image quality; Seam carving; Image (mathematics); Computer graphics (images); Bandwidth (computing)","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":[0.0001989292,0.0003760884,0.0004079233,0.0003968683,0.0001719331,0.0004066196,0.0007495236,0.0003904062,0.003527977],"category_scores_gemma":[0.0003979169,0.0002867051,0.0003312932,0.0002283668,0.000193155,0.0003481552,0.0004756915,0.0005526393,0.0006803705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002412284,"about_ca_system_score_gemma":0.0003034134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006713258,"about_ca_topic_score_gemma":0.0008998788,"domain_scores_codex":[0.9998362,0.00001911597,0.000006516163,0.00002401511,0.00009695994,0.00001719761],"domain_scores_gemma":[0.999801,0.00003664183,0.00002124651,0.00003785404,0.00007599209,0.00002734132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002073482,0.00008586467,0.000562072,0.0002151623,0.0000321703,0.0003700159,0.0001843277,0.01349455,0.6546816,0.009186283,0.004947015,0.3160337],"study_design_scores_gemma":[0.0001595238,0.0004783395,0.002163821,0.00004042594,0.00006253499,0.002444614,0.00005678063,0.4461093,0.5136097,0.003266346,0.03149537,0.0001132894],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01918064,0.000188869,0.9748217,0.00009187053,0.00008371467,0.0001077873,0.0001142105,0.002130093,0.003281157],"genre_scores_gemma":[0.2296746,0.000310482,0.7647015,0.00009407358,0.00005593474,0.0001053579,0.0002364775,0.0001767517,0.004644793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003527977,"threshold_uncertainty_score":0.01180226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0208860757127875,"score_gpt":0.2968592416689,"score_spread":0.2759731659561125,"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."}}