{"id":"W2177230683","doi":"10.1109/pacrim.2015.7334835","title":"Video editing in the gradient domain using a wavelet based 3-D reconstruction algorithm and an iterative Poisson solver","year":2015,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Computer science; Haar wavelet; Algorithm; Wavelet transform; Cascade algorithm; Iterative method; Solver; Noise (video); Wavelet packet decomposition; Computer vision; Discrete wavelet transform; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002144333,0.0001026601,0.0001145652,0.000116857,0.0001257812,0.0003515327,0.0002207219,0.00003968183,0.000003310043],"category_scores_gemma":[0.00005880923,0.00007021806,0.00002219329,0.0002914163,0.00005173853,0.0008729384,0.00005085927,0.0001278558,0.000001184684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000559784,"about_ca_system_score_gemma":0.00005721839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001997366,"about_ca_topic_score_gemma":0.00002080394,"domain_scores_codex":[0.9985377,0.0005997606,0.0001717781,0.0002796789,0.0002099485,0.0002010906],"domain_scores_gemma":[0.9994385,0.0001281119,0.00005653781,0.0002233858,0.00007999747,0.00007351424],"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":[0.00001891406,0.0000804218,0.0003966029,0.000007065436,0.000007899951,0.000118428,0.01982281,0.0001426251,0.003638475,0.004935076,0.0002764088,0.9705552],"study_design_scores_gemma":[0.0008453031,0.0001380324,0.0005122821,0.00003490491,0.000003774663,0.0002243628,0.001162175,0.9783349,0.002823949,0.0155902,0.000185905,0.0001442107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1172048,0.00002593914,0.8813502,0.0004528094,0.0002916142,0.0001063845,7.128393e-7,0.00003062347,0.0005369385],"genre_scores_gemma":[0.09916253,4.228368e-7,0.8998625,0.0007725891,0.0001776339,0.000003926855,0.000001187244,0.000004387954,0.00001486368],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9781923,"threshold_uncertainty_score":0.3389838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04898456315775519,"score_gpt":0.3083280085282954,"score_spread":0.2593434453705403,"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."}}