{"id":"W2067728284","doi":"10.1109/icassp.2002.5745384","title":"A neighborhood-blocks motion estimation technique using the pyramidal data structure","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Acoustics Speech and Signal Processing","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Motion estimation; Motion vector; Quarter-pixel motion; Motion (physics); Computer science; Artificial intelligence; Motion field; Computer vision; Structure from motion; Block-matching algorithm; Mathematics; Algorithm; 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":[0.0001870709,0.0001829885,0.0001224219,0.0001291751,0.0003264103,0.0007670854,0.001120347,0.00007233171,0.00009458119],"category_scores_gemma":[0.00007248075,0.0001385912,0.00001950657,0.0001955165,0.0001034156,0.001167723,0.0002289788,0.0003583904,0.000003628044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005312001,"about_ca_system_score_gemma":0.00005360183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006010303,"about_ca_topic_score_gemma":7.322682e-7,"domain_scores_codex":[0.9985361,0.00003498329,0.000254886,0.0004631285,0.0005020932,0.0002088508],"domain_scores_gemma":[0.9990363,0.00005690564,0.0001872577,0.000358769,0.0002883617,0.00007247266],"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":[0.000009931606,0.00005396553,0.00004840573,0.00001980762,0.00001226322,0.00002391426,0.0002215414,0.008006959,0.1270173,0.00399887,0.000191469,0.8603956],"study_design_scores_gemma":[0.0001648824,0.00002872135,0.00002291513,0.000118374,0.000009061124,0.0001896927,0.00004799613,0.980626,0.004068744,0.01449838,0.00005917121,0.0001660949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002238689,0.00005796183,0.9951952,0.001175227,0.0002376105,0.0001606027,0.00002468062,0.00009409943,0.0008159498],"genre_scores_gemma":[0.8225934,0.0000191368,0.1767612,0.0004003269,0.0001310454,0.000002925945,0.00001142724,0.000009202214,0.00007135937],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.972619,"threshold_uncertainty_score":0.7397022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08259622337124095,"score_gpt":0.3374149510310276,"score_spread":0.2548187276597866,"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."}}