{"id":"W4220891343","doi":"10.18280/ts.390114","title":"Application of Image Processing and Identification Technology for Digital Archive Information Management","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Identification (biology); Digital image processing; Image processing; Histogram; Color management; Adaptive histogram equalization; Information retrieval; Computer vision; Digital image; Projection (relational algebra); Histogram equalization; Document image processing; Computer graphics (images); Image (mathematics); Multimedia; Artificial intelligence; Image segmentation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001839512,0.00005716289,0.00006271631,0.0002433343,0.0002330418,0.0001475132,0.0002044786,0.00001223696,0.000002164938],"category_scores_gemma":[0.000003816755,0.00006085681,0.00001798304,0.0002487276,0.00005258495,0.001074608,0.0001124197,0.00004436439,0.000001175742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002478047,"about_ca_system_score_gemma":0.00002189754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.87399e-7,"about_ca_topic_score_gemma":9.725618e-8,"domain_scores_codex":[0.9993644,0.000008661852,0.0002416347,0.0001438338,0.0001495034,0.00009193599],"domain_scores_gemma":[0.9996028,0.00001047073,0.0002082843,0.00009500749,0.00006912163,0.00001433401],"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":[0.00001196702,0.0000364494,0.0001343561,0.0001453989,0.000007786617,1.218334e-7,0.0004537132,0.0001178363,0.00412001,0.01521467,0.00005367199,0.979704],"study_design_scores_gemma":[0.001364361,0.0002429677,0.002463841,0.00003166143,0.00003290289,0.00006683471,0.001797318,0.892818,0.01364317,0.07913253,0.008136924,0.0002694624],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00898297,0.00003039401,0.9899331,0.0003053912,0.00003824899,0.0003629891,0.00001854478,0.00007456669,0.0002538343],"genre_scores_gemma":[0.9315957,0.000001778365,0.06800256,0.00002228027,0.000009952834,0.0003013277,0.00004940171,0.000002771779,0.00001421823],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9794345,"threshold_uncertainty_score":0.2481669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004351580698971883,"score_gpt":0.2078319375860578,"score_spread":0.2034803568870859,"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."}}