{"id":"W1990639109","doi":"10.1109/isbmsb.2009.5133770","title":"Fast multi-frame motion estimation for video processing","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Motion estimation; Computer science; Quarter-pixel motion; Block-matching algorithm; Block (permutation group theory); Matching (statistics); Computer vision; Artificial intelligence; Frame (networking); Motion (physics); Exploit; Range (aeronautics); Structure from motion; Algorithm; Video processing; Mathematics; Video tracking","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":[0.0003790928,0.0005390378,0.0003594742,0.0007068426,0.0002326189,0.0004049987,0.0004781863,0.0005572638,0.003113823],"category_scores_gemma":[0.001258116,0.0002056181,0.0002516581,0.0009361334,0.0001569087,0.0007108095,0.000320454,0.0004840876,0.001265641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003320347,"about_ca_system_score_gemma":0.0003927796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002346735,"about_ca_topic_score_gemma":0.002954727,"domain_scores_codex":[0.9997765,0.00004992714,0.00001146623,0.00003438948,0.0001129861,0.00001472809],"domain_scores_gemma":[0.9997304,0.0000984965,0.00002593994,0.00005362798,0.00008309796,0.000008416313],"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":[0.0001551438,0.00003419041,0.000427554,0.0001663108,0.00004065485,0.00005672168,0.00004168441,0.04353231,0.05012334,0.008793093,0.005822381,0.8908066],"study_design_scores_gemma":[0.00002593439,0.00009717299,0.001383118,0.00003871989,0.00002519573,0.0001744626,0.00002057441,0.9347782,0.03249585,0.007418167,0.02351794,0.00002458047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005298647,0.002342823,0.9900475,0.00008639943,0.00007552274,0.00003272654,0.00008783202,0.0008172238,0.001211364],"genre_scores_gemma":[0.148253,0.002474656,0.8441678,0.0000700526,0.00008833076,0.00009485838,0.0004967547,0.00008608814,0.004268416],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003113823,"threshold_uncertainty_score":0.01041681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03337977343633983,"score_gpt":0.2974142313902052,"score_spread":0.2640344579538654,"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."}}