{"id":"W2047993625","doi":"10.1142/s0218126604001921","title":"A COMPUTATIONAL-RAM (C-RAM) ARCHITECTURE FOR REAL-TIME MESH-BASED VIDEO MOTION TRACKING PART 1: MOTION ESTIMATION","year":2004,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Motion estimation; Quarter-pixel motion; Computer science; Motion compensation; Block-matching algorithm; Tracking (education); Reference frame; Motion vector; Block (permutation group theory); Macroblock; Frame (networking); Node (physics); Frame rate; Computer vision; Match moving; Motion (physics); Artificial intelligence; Real-time computing; Video tracking; Algorithm; Video processing; Engineering; Mathematics; Telecommunications","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":[0.0007469679,0.0002073535,0.0004408098,0.0004031446,0.0002625995,0.0004726811,0.0005003389,0.0001118637,4.998464e-7],"category_scores_gemma":[0.00007030903,0.0001766108,0.0001663887,0.0002844089,0.00004723072,0.0005097787,0.00005307804,0.0001944243,0.000002019529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000146769,"about_ca_system_score_gemma":0.0001418459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000192124,"about_ca_topic_score_gemma":6.514487e-7,"domain_scores_codex":[0.9981046,0.0001122201,0.0007478659,0.0003074697,0.0004718511,0.0002560447],"domain_scores_gemma":[0.9981549,0.0002338659,0.0008935119,0.0002565689,0.0003399705,0.0001211373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005711603,0.0000539834,0.00001572913,0.00008982362,0.00002987883,0.00000595401,0.0002840587,0.6886252,0.000292742,0.007538463,0.0004499567,0.3026085],"study_design_scores_gemma":[0.001979864,0.0006449654,0.0008734344,0.001501825,0.00002404865,0.0004074617,0.00006790584,0.9752993,0.0005406893,0.0176964,0.0006858777,0.000278206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01940101,0.000332491,0.9766226,0.002223744,0.0008468973,0.000334213,0.000004219688,0.0002093695,0.00002548003],"genre_scores_gemma":[0.949581,0.00001616672,0.05008032,0.0001275748,0.0001504609,0.00001347683,0.000006006195,0.00001371575,0.00001133867],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.93018,"threshold_uncertainty_score":0.7201982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02350319249923453,"score_gpt":0.250848692737993,"score_spread":0.2273455002387585,"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."}}