{"id":"W2125586054","doi":"10.1109/icdar.1995.601961","title":"Extraction of reference lines from documents with grey-level background using sub-images of wavelets","year":2002,"lang":"en","type":"article","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Wavelet; Orthonormal basis; Artificial intelligence; Multiresolution analysis; Computer science; Wavelet transform; Pattern recognition (psychology); Grey level; Computer vision; Process (computing); Image (mathematics); Discrete wavelet transform; Physics","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":[0.00002869113,0.00008675079,0.0001166168,0.00004494475,0.00002523611,0.00001801025,0.00008288408,0.00004361915,0.0001407156],"category_scores_gemma":[0.000003312896,0.00007348548,0.00001558628,0.0001242966,0.00003271677,0.0002380274,0.0000132162,0.00006671058,0.000004101093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001889121,"about_ca_system_score_gemma":0.00000444502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002094941,"about_ca_topic_score_gemma":0.00001047677,"domain_scores_codex":[0.9995283,0.000004222894,0.0001817746,0.00009943741,0.0000982725,0.0000879679],"domain_scores_gemma":[0.999644,0.00002132568,0.00006275919,0.0001602058,0.00009241587,0.00001926636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005189268,0.00006916688,0.0003734416,0.0001041313,0.00002942431,0.000001013032,0.0000617731,0.0005447753,0.978085,0.0001676965,0.0006935937,0.01986478],"study_design_scores_gemma":[0.0001686236,0.00002248119,0.002649005,0.0001245075,0.00002941262,0.000005970538,0.00004379531,0.05780017,0.938094,0.0004790136,0.0004324075,0.0001506206],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6360742,0.0001739615,0.3596309,0.00001705186,0.00001484813,0.00007657557,0.00002333586,0.0001367253,0.003852407],"genre_scores_gemma":[0.8202826,0.0001094741,0.1794064,0.000004379114,0.00001498327,0.000005944834,0.000006710854,0.00001351939,0.0001560212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1842084,"threshold_uncertainty_score":0.2996651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07746144663425727,"score_gpt":0.2934745659543375,"score_spread":0.2160131193200802,"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."}}