{"id":"W2069713616","doi":"10.1142/s0218001400000519","title":"EDGE EXTRACTION OF IMAGES BY RECONSTRUCTION USING WAVELET DECOMPOSITION DETAILS AT DIFFERENT RESOLUTION LEVELS","year":2000,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Hong Kong Baptist University","keywords":"Wavelet; Stationary wavelet transform; Second-generation wavelet transform; Wavelet packet decomposition; Wavelet transform; Lifting scheme; Artificial intelligence; Discrete wavelet transform; Pattern recognition (psychology); Cascade algorithm; Mathematics; Computer science; Computer vision; Multiresolution analysis","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":[0.0004313617,0.0005339679,0.0007053541,0.001179032,0.0001901423,0.0006949235,0.0006091166,0.0006284329,0.001071495],"category_scores_gemma":[0.001213186,0.0004330299,0.0008194841,0.0008605754,0.0003281838,0.001310708,0.0005175553,0.0007890817,0.0009428191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001454498,"about_ca_system_score_gemma":0.0001888664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003492098,"about_ca_topic_score_gemma":0.0003599701,"domain_scores_codex":[0.9997211,0.00002907565,0.0000265371,0.00006084897,0.0001319264,0.00003042638],"domain_scores_gemma":[0.9995994,0.0001279938,0.00006079748,0.00008594595,0.0001114079,0.00001454116],"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":[0.0002708773,0.00005668617,0.0009474034,0.0003800092,0.00006291222,0.0002468607,0.0001700409,0.01475607,0.4074206,0.006929213,0.0009744922,0.5677849],"study_design_scores_gemma":[0.00006547452,0.0003295606,0.004976478,0.00007291091,0.0001708877,0.001654507,0.0001281632,0.4297571,0.5377715,0.008072388,0.01690749,0.00009363857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01705038,0.0002204145,0.9817046,0.00003427137,0.0000180788,0.00002831197,0.00003518535,0.000411682,0.0004970272],"genre_scores_gemma":[0.08520811,0.0006093201,0.9125921,0.00003291141,0.0000314122,0.00003964623,0.0001870632,0.0001364968,0.001163044],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001179032,"threshold_uncertainty_score":0.003584445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09892708885345777,"score_gpt":0.3566210637194712,"score_spread":0.2576939748660134,"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."}}