{"id":"W2126110375","doi":"10.1109/mwscas.2000.952896","title":"A maximum entropy Kalman filter for image compression","year":2002,"lang":"en","type":"article","venue":"","topic":"Statistical Mechanics and Entropy","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Kalman filter; Smoothing; Entropy (arrow of time); Image compression; Computer science; Data compression; Mean squared error; Artificial intelligence; Signal compression; Compression (physics); Algorithm; Computer vision; Mathematics; Image processing; Image (mathematics); Statistics","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":[0.0007190082,0.0005334993,0.0007267119,0.0003649481,0.0003960342,0.0005643236,0.0006433091,0.001013607,0.001696895],"category_scores_gemma":[0.002203202,0.0002645083,0.0004638201,0.0004961643,0.0005632291,0.001107592,0.0006347228,0.000982166,0.0006208967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005489562,"about_ca_system_score_gemma":0.0007263768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002919258,"about_ca_topic_score_gemma":0.002690619,"domain_scores_codex":[0.9995753,0.00009094779,0.00002511986,0.00009209045,0.0001922829,0.00002421998],"domain_scores_gemma":[0.9995964,0.0002251242,0.00003409021,0.00003974136,0.00009127391,0.00001342432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001914746,0.00006850965,0.0007147078,0.0002437159,0.0001076774,0.0001872822,0.0001106835,0.5145509,0.01795247,0.09461761,0.004060844,0.3671941],"study_design_scores_gemma":[0.00001509035,0.00004052473,0.0002154048,0.00001449395,0.00001361768,0.00004613897,0.00000448365,0.984087,0.002953479,0.008833509,0.003758556,0.0000176081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001239831,0.0003987587,0.9973096,0.000101343,0.00005940764,0.00001259224,0.0000180917,0.0001283797,0.0007321152],"genre_scores_gemma":[0.327241,0.002677596,0.6581975,0.0003474245,0.0005518369,0.0002552472,0.0002801328,0.0001052575,0.01034413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002919258,"threshold_uncertainty_score":0.005804539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0209357244986894,"score_gpt":0.2561690301229245,"score_spread":0.2352333056242351,"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."}}