{"id":"W1788308873","doi":"10.1007/s11045-015-0362-3","title":"Velocity filtering for attenuating moving artifacts in videos using an ultra-low complexity 3-D linear-phase IIR filter","year":2015,"lang":"en","type":"article","venue":"Multidimensional Systems and Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; University of Victoria","funders":"","keywords":"Infinite impulse response; Linear filter; Linear phase; Mathematics; 2D Filters; Low-pass filter; Filter (signal processing); Computer science; Robustness (evolution); Control theory (sociology); Digital filter; Algorithm; Acoustics; Computer vision; Physics; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002552091,0.000315005,0.0004934476,0.0001923314,0.0005325595,0.0004542535,0.000287239,0.0001269011,0.000001946725],"category_scores_gemma":[0.000199568,0.0002883765,0.00006449067,0.0003105191,0.00009286709,0.001614739,0.0001226869,0.0002497595,0.000002053655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000127354,"about_ca_system_score_gemma":0.0002462176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005432038,"about_ca_topic_score_gemma":0.00001458606,"domain_scores_codex":[0.9971383,0.0003591386,0.0006968651,0.000726031,0.0005017694,0.0005778929],"domain_scores_gemma":[0.9984067,0.0003194017,0.0002877526,0.0002396515,0.000414578,0.0003319263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000317118,0.0004878221,0.0007707774,0.0007980076,0.0000236352,0.0001455969,0.005507086,0.07009871,0.8311897,0.0004844238,0.00002705582,0.09015007],"study_design_scores_gemma":[0.002542397,0.0001610336,0.0002455822,0.000711312,0.000009740225,0.0001119457,0.0002728313,0.9739948,0.02100899,0.0004949998,0.00005520263,0.0003911574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.455664,0.0002650918,0.5435424,0.00003381177,0.0001368329,0.0002706315,0.000006004147,0.00006168168,0.00001949476],"genre_scores_gemma":[0.7178352,4.750357e-7,0.2817865,0.0001223149,0.0001875605,0.00001873101,0.000009502212,0.0000222048,0.0000174717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9038961,"threshold_uncertainty_score":0.9999568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1629028353577446,"score_gpt":0.3639973606853536,"score_spread":0.201094525327609,"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."}}