{"id":"W2111062549","doi":"10.1109/icip.2000.899373","title":"Efficient high-order image subsampling using FANNs","year":2002,"lang":"en","type":"article","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Image (mathematics); Artificial neural network; Feed forward; Artificial intelligence; Pattern recognition (psychology); Feedforward neural network; SIGNAL (programming language); Signal processing; Order (exchange); Image processing; Algorithm; Computer vision; Engineering; Digital signal processing; Computer hardware","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":[0.0006452915,0.0005762433,0.000505739,0.0005671975,0.0003044493,0.0004067782,0.0006913804,0.0004676995,0.001520944],"category_scores_gemma":[0.001138781,0.0002610681,0.0004038579,0.0003297969,0.0003897852,0.0007620041,0.0004515546,0.0004867375,0.0003504567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004504263,"about_ca_system_score_gemma":0.0004014921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001847414,"about_ca_topic_score_gemma":0.00344872,"domain_scores_codex":[0.9997393,0.00006140453,0.00001460547,0.00005479038,0.0001062344,0.00002355352],"domain_scores_gemma":[0.9996088,0.0001469374,0.00003392044,0.00008501046,0.0001029093,0.00002231433],"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":[0.0004270249,0.0001045835,0.0008779832,0.0001771231,0.00007253447,0.0001488264,0.000162693,0.1232637,0.2158297,0.01961408,0.002473927,0.6368479],"study_design_scores_gemma":[0.00001939608,0.00007005488,0.0003472352,0.000007805925,0.00001628366,0.0001106815,0.000009783804,0.9362776,0.05507391,0.004445229,0.003605094,0.00001698832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01353646,0.000112831,0.9850606,0.00003938538,0.00002611549,0.00002463203,0.00002052614,0.0004586762,0.0007206492],"genre_scores_gemma":[0.2074454,0.0001288747,0.7901442,0.00007367369,0.00004271354,0.00006740244,0.000112663,0.00008145436,0.001903605],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001847414,"threshold_uncertainty_score":0.005088031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02189755945504283,"score_gpt":0.2485047239318505,"score_spread":0.2266071644768076,"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."}}