{"id":"W1599773582","doi":"10.1109/icip.1996.561046","title":"A NTSC-compatible compact representation for stereoscopic sequences","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chrominance; NTSC; Ghosting; Computer science; Computer vision; Stereoscopy; Subcarrier; Channel (broadcasting); Composite video; Artificial intelligence; Luminance; Telecommunications; High-definition television","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002465154,0.0004737525,0.0002143447,0.0007541965,0.0002598941,0.0004744389,0.0004295005,0.0004275065,0.008926718],"category_scores_gemma":[0.0008524481,0.0001401499,0.0002233107,0.000792742,0.0002098839,0.0006305358,0.0002928751,0.0004379111,0.002783898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003599608,"about_ca_system_score_gemma":0.0004364338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001125846,"about_ca_topic_score_gemma":0.001702891,"domain_scores_codex":[0.99978,0.00002862959,0.00001709867,0.00002236869,0.0001338471,0.0000180843],"domain_scores_gemma":[0.9997253,0.00004828881,0.00003078399,0.00007324441,0.0001025215,0.00001992613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003795077,0.00005591672,0.0002161502,0.0002860753,0.00001477985,0.0004670736,0.0002227125,0.01301505,0.2409563,0.0662969,0.01399782,0.6640916],"study_design_scores_gemma":[0.0001024734,0.0005014592,0.001376772,0.0001462382,0.00004446842,0.002110681,0.0001387278,0.4107476,0.2314004,0.01560092,0.337745,0.00008530031],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007825195,0.0002292021,0.9838616,0.00005188087,0.0001676877,0.0001047312,0.0004277838,0.001569603,0.005762296],"genre_scores_gemma":[0.06441851,0.0003183086,0.9236634,0.00006112632,0.0001050423,0.0002309923,0.001505024,0.0003142191,0.009383342],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008926718,"threshold_uncertainty_score":0.02986288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09206480340155716,"score_gpt":0.3535657933446588,"score_spread":0.2615009899431016,"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."}}