{"id":"W2131286555","doi":"10.1109/tcsvt.2006.882390","title":"Fast Stereo Matching Algorithm for Intermediate View Reconstruction of Stereoscopic Television Images","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Pixel; Block (permutation group theory); Stereoscopy; Matching (statistics); Computer science; Artificial intelligence; Computer vision; Algorithm; Block size; Blossom algorithm; Similarity (geometry); Image resolution; Image (mathematics); Mathematics; Statistics","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":[0.0004401263,0.000398118,0.0006119235,0.001085527,0.0003798027,0.0004517897,0.001038269,0.0007070639,0.003622419],"category_scores_gemma":[0.000697536,0.0003409533,0.0006007138,0.0006567273,0.0002205452,0.0007482475,0.0005944876,0.0007138546,0.0009557816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005500399,"about_ca_system_score_gemma":0.0008304312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002052341,"about_ca_topic_score_gemma":0.002768434,"domain_scores_codex":[0.9995634,0.00004450263,0.0000198186,0.00005757805,0.0002805203,0.00003419008],"domain_scores_gemma":[0.9997588,0.00004518237,0.00002701778,0.0000361985,0.0001208458,0.00001199075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001725604,0.00006683906,0.0004363004,0.0001215765,0.00006239915,0.0001060477,0.00008919013,0.03284023,0.07385914,0.01197768,0.004624309,0.8756437],"study_design_scores_gemma":[0.00007557443,0.0001360109,0.00118464,0.00002039898,0.00004582051,0.0005254291,0.00003814212,0.9065881,0.06619011,0.005697621,0.01945512,0.00004307485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002414496,0.00006349968,0.9965923,0.00001916899,0.00001718812,0.00002447654,0.00002250043,0.0003080891,0.0005382763],"genre_scores_gemma":[0.04461063,0.0001246757,0.9532743,0.0000336531,0.00002083144,0.00009049092,0.0001578286,0.0000604428,0.001627127],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003622419,"threshold_uncertainty_score":0.01211816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01364949602941973,"score_gpt":0.2616486359682547,"score_spread":0.2479991399388349,"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."}}