{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003473783,0.0004920963,0.0004945132,0.0005625791,0.000188514,0.0003421321,0.0006605123,0.0005420165,0.001396918],"category_scores_gemma":[0.001598776,0.0003360228,0.0004077672,0.0006359854,0.0002356758,0.001128052,0.0007080596,0.0005846209,0.0006027716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002126135,"about_ca_system_score_gemma":0.0006415943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001512171,"about_ca_topic_score_gemma":0.003136803,"domain_scores_codex":[0.9997328,0.00002433784,0.00001913745,0.00004133639,0.0001514052,0.00003095622],"domain_scores_gemma":[0.9994355,0.0001637845,0.000109497,0.0001204288,0.0001492117,0.00002160651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002736078,0.00006323709,0.0008821543,0.0001707279,0.00009158919,0.0001161632,0.00009405636,0.06767248,0.2814187,0.006677662,0.001958564,0.6405811],"study_design_scores_gemma":[0.00006975495,0.0003306497,0.001797119,0.00003362706,0.00006832309,0.0006640378,0.00004398173,0.864791,0.1212763,0.004586512,0.006283635,0.00005496935],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01559393,0.0001994519,0.9831269,0.00003523491,0.00001745972,0.00002422027,0.00003950077,0.000339858,0.0006234341],"genre_scores_gemma":[0.1695206,0.0003926329,0.8280191,0.00004906561,0.00003607886,0.00006000566,0.0002113509,0.00004518675,0.001665895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001512171,"threshold_uncertainty_score":0.004673183,"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."}}