{"id":"W2011365580","doi":"10.1007/s11554-012-0275-4","title":"Real-time stereo using approximated joint bilateral filtering and dynamic programming","year":2012,"lang":"en","type":"article","venue":"Journal of Real-Time Image Processing","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Bilateral filter; Frame rate; Dynamic programming; Filter (signal processing); Frame (networking); Matching (statistics); Pixel; Graphics hardware; Computational complexity theory; Artificial intelligence; Computer vision; Algorithm; Computer graphics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001114274,0.0003285938,0.0005481081,0.0004291587,0.0003397137,0.0009008444,0.0004573253,0.00007482012,0.0000231399],"category_scores_gemma":[0.00009296887,0.0002742245,0.0001228673,0.0005000669,0.0001174992,0.006352147,0.0003809696,0.0003512885,0.00001470426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001724368,"about_ca_system_score_gemma":0.0001439508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008734964,"about_ca_topic_score_gemma":7.281992e-8,"domain_scores_codex":[0.9973965,0.0001088532,0.0009134492,0.000330524,0.0004802423,0.0007704642],"domain_scores_gemma":[0.9978863,0.00005578371,0.00104253,0.0002866843,0.0003552996,0.0003734065],"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":[0.00002048047,0.00007227984,0.0001885099,0.0001478006,0.00001802458,0.00005804791,0.001030839,0.00003861271,0.7452568,0.00000510836,0.00002555531,0.2531379],"study_design_scores_gemma":[0.000956267,0.0001426476,0.001497843,0.00144456,0.00005734125,0.003561466,0.0001879483,0.9698819,0.02120518,0.0002656466,0.0002263595,0.0005728325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4243825,0.0006583281,0.5734631,0.0002259163,0.0002065205,0.0002041984,0.000001289761,0.0002110887,0.0006470638],"genre_scores_gemma":[0.1652173,0.0001167621,0.8342737,0.00003739283,0.0001488122,0.000001811113,0.00000127991,0.00004702785,0.0001558981],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9698433,"threshold_uncertainty_score":0.999971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01827647898164557,"score_gpt":0.2970897508694222,"score_spread":0.2788132718877766,"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."}}