{"id":"W2095763972","doi":"10.1109/ccece.2008.4564754","title":"The nonredundant contourlet transform (NRCT): A multiresolution and multidirection image representation","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Contourlet; Wavelet transform; Artificial intelligence; Redundancy (engineering); Mathematics; Computer vision; Filter bank; Pattern recognition (psychology); Wavelet; Computer science; Filter (signal processing)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00009952308,0.0002832581,0.000232574,0.0002220299,0.0003767925,0.0002206938,0.0001555661,0.000118068,0.000007586102],"category_scores_gemma":[0.00005151959,0.0002512726,0.00003611187,0.000270089,0.0001108984,0.000351007,0.00002150121,0.0004067875,0.00000376597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001683685,"about_ca_system_score_gemma":0.00008122573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001268723,"about_ca_topic_score_gemma":0.000676684,"domain_scores_codex":[0.9986804,0.000007377314,0.0002612215,0.0003492468,0.0001702851,0.0005314624],"domain_scores_gemma":[0.9992312,0.0000714564,0.00003596616,0.00009762361,0.0002007763,0.0003630281],"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":[0.00009941174,0.0000522368,0.00100864,0.0001965279,0.0001226955,0.0000827551,0.003856302,0.0007789073,0.0764487,0.02759359,0.003437032,0.8863232],"study_design_scores_gemma":[0.000306211,0.000141813,0.004754247,0.00007544265,0.000008811483,0.0001327779,0.00004571668,0.984201,0.006953295,0.0003470737,0.002688793,0.0003448485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3458114,0.0007972906,0.6443488,0.001397545,0.0004439549,0.001351621,0.00001929275,0.001706982,0.004123096],"genre_scores_gemma":[0.9924712,0.001895707,0.005298427,0.00004842164,0.00009789821,0.00009020967,0.000005115223,0.00003204021,0.00006104783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.983422,"threshold_uncertainty_score":0.999994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01243340946611647,"score_gpt":0.2062582904308115,"score_spread":0.193824880964695,"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."}}