{"id":"W2109244936","doi":"10.1109/tip.2011.2134106","title":"Lookup-Table-Based Gradient Field Reconstruction","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Engineering and Physical Sciences Research Council","keywords":"Lookup table; Algorithm; Field (mathematics); Computer science; Iterative reconstruction; Image gradient; Vector field; Scalar field; Mathematics; Artificial intelligence; Computer vision; Image (mathematics); Image processing; Geometry; Edge detection","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":[0.0003771055,0.0004623966,0.0005262192,0.0005137929,0.0002447487,0.001127097,0.0008771991,0.0006351737,0.006429534],"category_scores_gemma":[0.001418004,0.0003165003,0.0003598068,0.0005839666,0.000370068,0.001209209,0.0006677694,0.000674052,0.002788631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003969165,"about_ca_system_score_gemma":0.0007106074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001570707,"about_ca_topic_score_gemma":0.002271199,"domain_scores_codex":[0.9998544,0.00002403736,0.000009912027,0.00003217818,0.0000631111,0.00001636591],"domain_scores_gemma":[0.9997553,0.00007092246,0.00001709913,0.00006300748,0.00008074011,0.00001300394],"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":[0.0003976672,0.0000967781,0.001328954,0.0004338191,0.00006201594,0.000352009,0.0002804635,0.2230089,0.06538381,0.116958,0.01364643,0.5780511],"study_design_scores_gemma":[0.0000232875,0.00006504426,0.0001914021,0.00002133992,0.00001250309,0.0002877152,0.00003355108,0.9402335,0.02810303,0.01839378,0.01260341,0.00003141456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004080752,0.0001480884,0.992288,0.00006653746,0.00004038315,0.00002752038,0.0001055971,0.001247149,0.001996044],"genre_scores_gemma":[0.1064786,0.0002854973,0.8884653,0.00009992102,0.00002670856,0.00004877031,0.0003652928,0.0002992475,0.003930634],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006429534,"threshold_uncertainty_score":0.02150887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02558094945147743,"score_gpt":0.258420212160799,"score_spread":0.2328392627093215,"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."}}