{"id":"W42537546","doi":"10.2352/cic.2004.12.1.art00017","title":"Analysis of Spatio-chromatic Decorrelation for Colour Image Reconstruction","year":2004,"lang":"en","type":"article","venue":"Color and Imaging Conference","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Decorrelation; Chromatic scale; Computer science; Artificial intelligence; Computer vision; Encoding (memory); Basis (linear algebra); Discrete cosine transform; Data compression; Iterative reconstruction; Spatial analysis; Human visual system model; Image compression; Pattern recognition (psychology); Perception; Image (mathematics); Mathematics; Image processing","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":[0.0002387516,0.00007334349,0.0001699497,0.0002564096,0.00007613236,0.0001211267,0.0001575593,0.00002459222,0.000007797437],"category_scores_gemma":[0.00008842568,0.00007431754,0.00005165281,0.0004010578,0.00007381619,0.0005899371,0.00003964564,0.00004448042,0.000001380643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000029688,"about_ca_system_score_gemma":0.00009338406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003311454,"about_ca_topic_score_gemma":0.00004386734,"domain_scores_codex":[0.9993722,0.00002943418,0.0002195244,0.0002019999,0.00008313588,0.00009370869],"domain_scores_gemma":[0.9992914,0.00008826425,0.0001635593,0.0001798491,0.0002441299,0.00003279993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004525108,0.0001843357,0.03582701,0.0001442952,0.0003881009,0.000002772326,0.008941549,0.001794635,0.04655029,0.6276278,0.0002333244,0.2782606],"study_design_scores_gemma":[0.0004180284,0.00008342582,0.04996503,0.0000331421,0.0001415123,0.000009416234,0.00009070717,0.9173613,0.007955172,0.02370959,0.00008801716,0.0001446563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2659971,0.00001094372,0.7326381,0.0007006674,0.00004838851,0.0001614755,0.000004193696,0.00007586214,0.0003632981],"genre_scores_gemma":[0.8214654,0.000007998472,0.1783881,0.00008462246,0.000004056012,0.00002345926,0.00001033958,0.000002560739,0.00001335837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9155667,"threshold_uncertainty_score":0.3030581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01379608198829993,"score_gpt":0.2707887986857839,"score_spread":0.256992716697484,"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."}}