{"id":"W2159550985","doi":"10.1109/icip.1997.632196","title":"Motion compensation in color video with illumination variations","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Motion compensation; Artificial intelligence; Quarter-pixel motion; Computer science; Block-matching algorithm; Motion estimation; Compensation (psychology); Motion vector; Motion (physics); Block (permutation group theory); Matching (statistics); Mathematics; Image (mathematics); Video processing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008761074,0.00004856888,0.00004997467,0.0001208623,0.00006044234,0.00005650364,0.0001198843,0.00001505566,0.00009301682],"category_scores_gemma":[0.00002692997,0.00004062942,0.000008299469,0.0004197947,0.0000125566,0.0009601238,0.00002540689,0.00004572024,0.00007942009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004893707,"about_ca_system_score_gemma":0.000004637426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001453345,"about_ca_topic_score_gemma":0.00003761405,"domain_scores_codex":[0.9994804,0.0000264814,0.0001085944,0.0001610957,0.0001310733,0.00009232402],"domain_scores_gemma":[0.9996864,0.00003824372,0.00004110253,0.0001503312,0.00005992418,0.00002399955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003111337,0.0002627653,0.003400165,0.000005458891,0.00000344159,0.000006401691,0.00205244,0.00461968,0.001433493,0.502898,0.0005293513,0.4847857],"study_design_scores_gemma":[0.0003088135,0.00002920251,0.02837867,0.000007690149,6.592045e-7,0.000004473906,0.0000307372,0.9687734,0.000331087,0.001331976,0.0007399638,0.00006336701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003528819,0.000006379834,0.9830061,0.002222199,0.00005531784,0.0001061074,1.457568e-7,0.00009733446,0.01097761],"genre_scores_gemma":[0.8141493,0.000003334647,0.1850376,0.0003003313,0.000007872489,0.000007257351,0.00000150353,0.000002317397,0.000490545],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9641537,"threshold_uncertainty_score":0.165682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01802361873275432,"score_gpt":0.2425947511917041,"score_spread":0.2245711324589497,"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."}}