{"id":"W2041939476","doi":"10.1117/12.2010390","title":"Edge adaptive intra field de-interlacing of video images","year":2013,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Qualcomm (Canada)","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Image scaling; Interlacing; Interpolation (computer graphics); Pixel; Filter (signal processing); Image processing; Enhanced Data Rates for GSM Evolution; Algorithm; Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002509618,0.0005181733,0.0003314372,0.000721163,0.0002243714,0.000412314,0.0004527229,0.0003313696,0.001786363],"category_scores_gemma":[0.0007467954,0.0001887159,0.0003698478,0.0005265287,0.0002398437,0.0007173311,0.000324503,0.000479263,0.0006527462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002019458,"about_ca_system_score_gemma":0.0002446547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006649198,"about_ca_topic_score_gemma":0.00114467,"domain_scores_codex":[0.9998093,0.00001790468,0.0000131234,0.00004855128,0.00008815731,0.00002296644],"domain_scores_gemma":[0.9995794,0.00007458738,0.00005670543,0.0000940762,0.0001752004,0.00002013366],"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":[0.0003470731,0.00009281909,0.001005217,0.0002079205,0.00003435351,0.0002432607,0.0001719225,0.01062427,0.458328,0.004311059,0.002481376,0.5221528],"study_design_scores_gemma":[0.00002821994,0.0003850971,0.004581012,0.00003280546,0.00005504055,0.0009331678,0.0001062082,0.2919183,0.6792882,0.002007394,0.02061448,0.00004999994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08270801,0.0006833886,0.9110277,0.00009877648,0.0001224488,0.00009288896,0.0001030982,0.001065044,0.004098754],"genre_scores_gemma":[0.283126,0.0009112713,0.7045001,0.0001062127,0.00008040042,0.00007391846,0.0003598743,0.0002219925,0.01062019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001786363,"threshold_uncertainty_score":0.005975962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01211942801357459,"score_gpt":0.2401990433634153,"score_spread":0.2280796153498407,"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."}}