{"id":"W2368193714","doi":"","title":"Improvement to MVFAST Motion Estimation Algorithm and VLSI Architecture","year":2008,"lang":"en","type":"article","venue":"Computer Technology and Development","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Computer science; Motion estimation; Algorithm; Architecture; Search algorithm; Bandwidth (computing); Image quality; Very-large-scale integration; Image processing; Artificial intelligence; Image (mathematics); Computer vision; Real-time computing; Embedded system","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":[],"consensus_categories":[],"category_scores_codex":[0.00006868043,0.0001253504,0.0001194433,0.0003746059,0.0002705482,0.00003390434,0.0002051447,0.00006173999,0.000001018815],"category_scores_gemma":[0.000006200083,0.0001131624,0.000007741675,0.0002976778,0.00006485719,0.0001524726,0.0005670707,0.0001200643,0.00001347874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002800671,"about_ca_system_score_gemma":0.00002841975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.300361e-7,"about_ca_topic_score_gemma":2.693978e-7,"domain_scores_codex":[0.9991572,0.000007716572,0.000163718,0.0003770403,0.0001019233,0.0001924214],"domain_scores_gemma":[0.9996377,0.00001223124,0.00003834679,0.000195413,0.00003968582,0.00007660511],"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":[8.449242e-7,0.00001685298,0.0001912256,0.000004484326,0.000004760968,0.00001055701,0.0005209551,0.0001027853,0.0001607712,0.0009671632,0.00008566031,0.9979339],"study_design_scores_gemma":[0.00143888,0.0004959149,0.0410991,0.0001258335,0.000003967729,0.00112862,0.00005538092,0.841915,0.03441393,0.01252886,0.06592081,0.0008737413],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02050499,0.00009722227,0.9763878,0.002415741,0.0001383395,0.0001587645,2.514278e-7,0.0002796307,0.00001726896],"genre_scores_gemma":[0.06912711,0.00001541112,0.9300336,0.000743606,0.00001233222,0.00001322861,0.000001705798,0.000004314647,0.00004869838],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9970602,"threshold_uncertainty_score":0.461463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007951911162013353,"score_gpt":0.2250856023072665,"score_spread":0.2171336911452531,"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."}}