{"id":"W2148172295","doi":"10.1007/978-3-642-02611-9_8","title":"Image Resolution Enhancement with Hierarchical Hidden Fields","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Scale (ratio); Fractal; Image processing; Scale space; Markov chain; Pattern recognition (psychology); Key (lock); Mathematical morphology; Grayscale; Hidden Markov model; Hierarchical database model; Image (mathematics); Computer vision; Algorithm; Data mining; Machine learning; Mathematics; Cartography","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":[0.0003507961,0.0004262481,0.0004082373,0.00039904,0.0001445933,0.0004432322,0.0005090796,0.0005562227,0.002514727],"category_scores_gemma":[0.0005869833,0.0003128997,0.0004264891,0.0003512941,0.0003583169,0.0008966685,0.000692374,0.0008176854,0.0005011351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002310337,"about_ca_system_score_gemma":0.000207582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005910982,"about_ca_topic_score_gemma":0.001057128,"domain_scores_codex":[0.9999086,0.000015154,0.000004384764,0.00001912998,0.00003773674,0.00001492764],"domain_scores_gemma":[0.9997942,0.0000923539,0.00001873898,0.00004651434,0.00003368866,0.0000145759],"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":[0.0004392086,0.0001380396,0.0004686228,0.0003572255,0.0001069342,0.0002236968,0.000122193,0.1643063,0.2837844,0.04540697,0.004062641,0.5005838],"study_design_scores_gemma":[0.00002053336,0.00007392118,0.0004019043,0.00002305477,0.00003410161,0.0001978543,0.00001058747,0.9252574,0.05689233,0.01405099,0.003022757,0.00001439207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01353985,0.0004077689,0.9838509,0.00008429658,0.00003804694,0.00001716829,0.00003708705,0.000319884,0.00170508],"genre_scores_gemma":[0.2960974,0.000971618,0.6912606,0.0001680062,0.00009248182,0.00003388024,0.0002012495,0.000169309,0.01100549],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002514727,"threshold_uncertainty_score":0.0084126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01605650393417736,"score_gpt":0.2654056754331722,"score_spread":0.2493491714989949,"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."}}