{"id":"W2112228563","doi":"10.1109/icpr.2002.1048189","title":"An accurate discrete Fourier transform for image processing","year":2003,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Discrete Fourier transform (general); Non-uniform discrete Fourier transform; Discrete-time Fourier transform; Fractional Fourier transform; Fourier transform; Harmonic wavelet transform; Short-time Fourier transform; Algorithm; Fast Fourier transform; Discrete sine transform; Hartley transform; Cyclotomic fast Fourier transform; Prime-factor FFT algorithm; Multidimensional signal processing; Fourier analysis; Fourier transform on finite groups; Computer science; Discrete Hartley transform; Mathematics; Mathematical analysis; Signal processing; Computer vision; Digital signal processing; Wavelet transform; Discrete wavelet transform","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":[0.0007562524,0.0001186257,0.0001250082,0.00005204181,0.0002081815,0.0005058958,0.0004367214,0.00003992318,0.00002355206],"category_scores_gemma":[0.00005193006,0.00009052051,0.00006714494,0.0002084958,0.00003072704,0.001882738,0.00001130097,0.00006551325,0.000008701933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001270521,"about_ca_system_score_gemma":0.00008472183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003937836,"about_ca_topic_score_gemma":0.000002351564,"domain_scores_codex":[0.9989899,0.00008189928,0.0001664558,0.0003072758,0.0001451623,0.0003092709],"domain_scores_gemma":[0.9993832,0.00006054482,0.0000356414,0.0003194988,0.0001030108,0.0000981716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000275183,0.00006869785,0.000007048151,0.00006092265,0.00001072136,0.00002042824,0.001439849,0.00003499712,0.03608701,0.05560067,0.0007272467,0.9059149],"study_design_scores_gemma":[0.001732107,0.000340224,0.00006270823,0.00002718239,0.000020527,0.00006221526,0.0001155056,0.3860991,0.4654588,0.1131671,0.03231212,0.0006024271],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00041788,0.00004918063,0.9746686,0.0004602528,0.000115086,0.0002005715,0.000001127055,0.0001477458,0.02393962],"genre_scores_gemma":[0.1102764,0.000001873319,0.8874832,0.0005065979,0.00003688881,0.0000215951,0.000001293908,0.00001172618,0.001660378],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9053125,"threshold_uncertainty_score":0.4878365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02846106162186912,"score_gpt":0.333315397320071,"score_spread":0.3048543356982019,"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."}}