{"id":"W2138091401","doi":"10.1109/siecpc.2011.5876951","title":"Determining suitable wavelet filters for visual sensor networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Wavelet; Discrete wavelet transform; Lifting scheme; Computer science; Computer vision; Artificial intelligence; Second-generation wavelet transform; Wavelet packet decomposition; Wavelet transform; Energy (signal processing); Stationary wavelet transform; Mathematics","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":[0.0005824011,0.0001133836,0.0001433628,0.00006062123,0.0001325088,0.0001250486,0.0004310973,0.00006196782,0.00005101818],"category_scores_gemma":[0.00005718048,0.00009648418,0.00007717528,0.0001474275,0.00002278028,0.0003911362,0.0001292802,0.00007811701,0.00001941415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001220598,"about_ca_system_score_gemma":0.00002440154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002133393,"about_ca_topic_score_gemma":0.000001803696,"domain_scores_codex":[0.9989742,0.00007237215,0.0001704768,0.0002874415,0.000100077,0.0003954576],"domain_scores_gemma":[0.9992952,0.0002396902,0.0000467674,0.0002703191,0.00006631459,0.00008170182],"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":[0.0001245608,0.0002074306,0.001187855,0.00004995475,0.00009161995,0.0001826929,0.004355942,0.0005266559,0.008588872,0.02176971,0.01526647,0.9476482],"study_design_scores_gemma":[0.00066508,0.0002932172,0.0009822021,0.00001496064,0.000009881324,0.00003751291,0.00004331324,0.9679315,0.02676166,0.001118827,0.001847603,0.0002942337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004738643,0.00001631298,0.9863994,0.00002924834,0.0004348108,0.0001234237,3.981714e-7,0.0001505423,0.008107186],"genre_scores_gemma":[0.1544625,0.00000138871,0.8408927,0.0009038865,0.0001021097,0.0000133489,0.000001033157,0.00001127573,0.003611797],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9674048,"threshold_uncertainty_score":0.3934511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0575444977255483,"score_gpt":0.2944107136751857,"score_spread":0.2368662159496374,"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."}}