{"id":"W2116576559","doi":"10.1109/tcsii.2003.808894","title":"Pyramidal motion estimation techniques exploiting intra-level motion correlation","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Motion estimation; Motion field; Motion (physics); Computer science; Quarter-pixel motion; Motion vector; Artificial intelligence; Scaling; Structure from motion; Linear motion; Algorithm; Correlation; Block (permutation group theory); Computer vision; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002642394,0.0001899161,0.0001885539,0.0002186073,0.0008164393,0.0008913731,0.0000812056,0.0000872643,0.000001925754],"category_scores_gemma":[0.00001536987,0.0001777171,0.00003871175,0.000309808,0.00006161586,0.003068339,0.000002820763,0.0001854963,0.000002137929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000519872,"about_ca_system_score_gemma":0.00003297703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007770071,"about_ca_topic_score_gemma":8.049103e-7,"domain_scores_codex":[0.9987036,0.00004954651,0.0003582434,0.0004121803,0.0002546454,0.0002218112],"domain_scores_gemma":[0.9994384,0.00005820289,0.0001563054,0.0001100393,0.0001211895,0.0001158634],"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":[0.000001868181,0.00005267486,0.00004693756,0.00006085885,0.000005127933,0.000001990633,0.000363408,0.006086614,0.001247411,0.00153839,0.000001888147,0.9905928],"study_design_scores_gemma":[0.0002772867,0.0001242946,0.0001186453,0.000344205,0.0000121808,0.000152491,0.0003272046,0.9920501,0.003966178,0.002257208,0.00009542839,0.0002747428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006071335,0.0002363817,0.9918851,0.00003449135,0.0001480083,0.0001911358,0.000006311594,0.0002309092,0.001196349],"genre_scores_gemma":[0.997468,0.00001765367,0.002265188,0.00004667889,0.00002597662,0.00001859838,0.000004207772,0.00001340201,0.0001403246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9913967,"threshold_uncertainty_score":0.8595532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03029637369448778,"score_gpt":0.2532565285590046,"score_spread":0.2229601548645168,"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."}}