{"id":"W196696935","doi":"","title":"THE EFFECTS OF DIFFERENT TYPES OF WAVELETS ON IMAGE FUSION","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Wavelet; Image fusion; Artificial intelligence; Wavelet transform; Multispectral image; Computer vision; Biorthogonal wavelet; Biorthogonal system; Transformation (genetics); Stationary wavelet transform; Pattern recognition (psychology); Wavelet packet decomposition; Multiresolution analysis; Top-hat transform; Panchromatic film; Computer science; Discrete wavelet transform; Orthogonal wavelet; Second-generation wavelet transform; Mathematics; Image processing; Binary image; Image (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":[0.00002529273,0.00007519238,0.0001029726,0.00003083903,0.00001907552,0.00000290582,0.00009028097,0.00002561433,0.00001764609],"category_scores_gemma":[0.00005986022,0.00004342268,0.00003417298,0.00005140137,0.00003371131,0.00002903021,0.00002934571,0.00006019914,0.000005880688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002216154,"about_ca_system_score_gemma":0.000002437742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000431751,"about_ca_topic_score_gemma":0.000003074634,"domain_scores_codex":[0.9996318,0.000006465696,0.0001209954,0.00005442476,0.00009940403,0.00008695552],"domain_scores_gemma":[0.9996285,0.0001210344,0.00002286082,0.0001871189,0.0000237539,0.00001676472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006630083,0.00002779819,0.000006441019,0.0001021113,0.000006764755,0.000001500264,0.00003844371,0.0001336893,0.9712911,0.002087843,0.0002493649,0.02604832],"study_design_scores_gemma":[0.0001559578,0.0001171961,0.0009760844,0.00008481496,0.00000326809,4.373758e-7,0.000006051949,0.0002110258,0.9951032,0.003179505,0.0001150254,0.00004745746],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.881312,0.0002626679,0.1025117,0.00006170611,0.0002216733,0.0003622412,0.000001966407,0.0004269069,0.01483921],"genre_scores_gemma":[0.9943507,0.0003250749,0.005166183,0.00001026772,0.000009498534,0.000008094787,6.523338e-7,0.00001438972,0.0001151439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1130388,"threshold_uncertainty_score":0.1770725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002181289362419672,"score_gpt":0.2046310878814362,"score_spread":0.2024497985190165,"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."}}