{"id":"W1751402335","doi":"10.1109/iscas.2003.1205981","title":"A continuous tracking algorithm for long-term memory motion estimation [video coding]","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Motion estimation; Computer science; Reference frame; Coding (social sciences); Motion vector; Quarter-pixel motion; Computer vision; Block-matching algorithm; Artificial intelligence; Term (time); Inter frame; Motion compensation; Frame (networking); Tracking (education); Fast motion; Residual frame; Algorithm; Match moving; Motion (physics); Video tracking; Video processing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000825925,0.0005838171,0.0005705941,0.001149173,0.0005647447,0.0006887961,0.001112572,0.0008971837,0.002390591],"category_scores_gemma":[0.002335692,0.000307834,0.0004272753,0.001486336,0.0005568794,0.001261844,0.0006454409,0.001335372,0.001122585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005202323,"about_ca_system_score_gemma":0.001020278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003911575,"about_ca_topic_score_gemma":0.003884881,"domain_scores_codex":[0.9996158,0.00006439519,0.00002171404,0.00007844,0.0001921396,0.00002761574],"domain_scores_gemma":[0.9991025,0.0002768414,0.00007892025,0.0001327085,0.0003740523,0.00003495538],"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":[0.0001989221,0.00006383558,0.0004394619,0.0001289391,0.00005695963,0.00006355858,0.00007328557,0.02956875,0.03582741,0.01492665,0.005274558,0.9133777],"study_design_scores_gemma":[0.000102765,0.0002299346,0.0013478,0.0000626683,0.00005568139,0.0004455045,0.00003036881,0.9320862,0.03426874,0.007354716,0.02394283,0.00007279954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001946084,0.0006208292,0.9962679,0.00004438747,0.00008920305,0.00003374947,0.00002459472,0.0003904902,0.0005828714],"genre_scores_gemma":[0.06016459,0.0008632356,0.934988,0.00009755585,0.00009233056,0.0001682173,0.000266946,0.00008979376,0.003269239],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003911575,"threshold_uncertainty_score":0.007997334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0292072970806188,"score_gpt":0.2758633435090534,"score_spread":0.2466560464284346,"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."}}