{"id":"W2000981832","doi":"10.1117/12.571947","title":"Nonuniform smoothing of depth maps before image-based rendering","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Optical Imaging Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Smoothing; Computer vision; Artificial intelligence; Computer science; Image quality; Gaussian blur; Rendering (computer graphics); Stereoscopy; Edge-preserving smoothing; Autostereoscopy; Image (mathematics); Image processing; Image restoration","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":[0.0007892483,0.0005104321,0.0003598701,0.0005285912,0.0002062435,0.0007825479,0.0004035887,0.0003207435,0.004119608],"category_scores_gemma":[0.006230934,0.0002345885,0.0003669044,0.0004354718,0.0002955511,0.00068981,0.0006206098,0.0006859897,0.0005097934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00031133,"about_ca_system_score_gemma":0.0004255766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001850304,"about_ca_topic_score_gemma":0.002362254,"domain_scores_codex":[0.9994941,0.00009553878,0.00003269926,0.00009716739,0.0002191345,0.00006149059],"domain_scores_gemma":[0.9979385,0.001062676,0.0001361615,0.0003341953,0.0004622139,0.00006628122],"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":[0.0006622037,0.0001332489,0.002387894,0.0004978106,0.00004429428,0.000231649,0.001092773,0.01056356,0.7415586,0.004177019,0.001415151,0.2372359],"study_design_scores_gemma":[0.0001967658,0.001632564,0.04939498,0.0001153381,0.0001958872,0.001178241,0.000609663,0.2140791,0.697161,0.008521562,0.02668032,0.0002345564],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3464048,0.0004991509,0.6440706,0.000128003,0.0001306725,0.0004707496,0.0002307087,0.001779348,0.006286078],"genre_scores_gemma":[0.7194406,0.0003547676,0.2772706,0.00006534794,0.00003029348,0.0001878378,0.0001365909,0.0003220657,0.002191829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004119608,"threshold_uncertainty_score":0.01378143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00998275921729388,"score_gpt":0.2263867362214617,"score_spread":0.2164039770041678,"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."}}