{"id":"W2061539841","doi":"10.1109/tip.2013.2290586","title":"A MAP-Based Image Interpolation Method via Viterbi Decoding of Markov Chains of Interpolation Functions","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Interpolation (computer graphics); Stairstep interpolation; Demosaicing; Image scaling; Nearest-neighbor interpolation; Bilinear interpolation; Mathematics; Algorithm; Bicubic interpolation; Artificial intelligence; Viterbi algorithm; Computer science; Linear interpolation; Pattern recognition (psychology); Computer vision; Image processing; Decoding methods; Image (mathematics); Color image","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007725082,0.0003108855,0.0004039742,0.0008196784,0.0002986437,0.0001785766,0.000610842,0.0001204544,0.00003301194],"category_scores_gemma":[0.00006072598,0.0003253516,0.000185304,0.0008565577,0.0001901221,0.002462057,0.00001252755,0.0003695266,0.000008826984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009815512,"about_ca_system_score_gemma":0.0001305244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002258688,"about_ca_topic_score_gemma":0.000009639091,"domain_scores_codex":[0.9977062,0.000209641,0.0007800906,0.0005875758,0.0003883449,0.0003281962],"domain_scores_gemma":[0.9977258,0.0002985106,0.0006698056,0.0006069746,0.0006080334,0.00009089296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005943909,0.0001858936,0.00001105532,0.0003624682,0.00001408177,7.546855e-7,0.0005049109,0.0003046413,0.6388972,0.00004503158,0.00001584739,0.3595987],"study_design_scores_gemma":[0.0002958854,0.0001325148,0.00001594418,0.0003739649,0.00002976397,0.000008362896,0.00003695928,0.627075,0.3705668,0.001258872,0.00002182414,0.0001841562],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002502,0.00004254608,0.9979243,0.0003081987,0.0002605129,0.0002595655,0.000013149,0.000533792,0.0004077723],"genre_scores_gemma":[0.4498272,0.000001460845,0.549976,0.0000720649,0.00001793875,0.00003536739,0.000002579949,0.00002568416,0.00004170892],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6267703,"threshold_uncertainty_score":0.9999198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01217955256141935,"score_gpt":0.2962502302134458,"score_spread":0.2840706776520265,"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."}}