{"id":"W1548437120","doi":"10.1109/ccece.2015.7129347","title":"A comparative study of directional filters with application to angiogram image enhancement","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Contourlet; Computer vision; Decimation; Artificial intelligence; Computer science; Filter bank; Filter (signal processing); Context (archaeology); Noise (video); Upsampling; Image (mathematics); Pattern recognition (psychology); Geology; Wavelet transform","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":[0.00004359648,0.00007479955,0.0001125855,0.00007196555,0.00001117387,0.000005911191,0.00005946253,0.000009386606,0.00002333638],"category_scores_gemma":[0.000002936992,0.00006152061,0.00001063858,0.0002211292,0.00001279858,0.0000851568,0.00002038054,0.00003114912,0.00001435419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004837663,"about_ca_system_score_gemma":0.000004996013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000404924,"about_ca_topic_score_gemma":0.0000403697,"domain_scores_codex":[0.9995598,0.000007650343,0.0001122339,0.0001018801,0.0001447024,0.00007372816],"domain_scores_gemma":[0.9996744,0.00001047943,0.00001883927,0.0001369403,0.0001044076,0.00005493647],"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":[0.0004039449,0.002667429,0.002835217,0.00006253764,0.0003710579,0.000005999003,0.02051394,0.07767788,0.7601602,0.0001531177,0.08952809,0.04562057],"study_design_scores_gemma":[0.0006462657,0.001401534,0.00127656,0.00001486268,0.00001543007,0.000001934436,0.004405733,0.007819672,0.9807557,0.00009554538,0.003337452,0.0002293182],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2179875,0.000007605734,0.774646,0.00000923059,0.00001577901,0.0005851099,0.000001928652,0.0002783591,0.006468548],"genre_scores_gemma":[0.8569852,6.613427e-7,0.1427015,0.00001017249,0.000008296277,0.0001776363,0.000003879089,0.000008155576,0.0001045353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6389977,"threshold_uncertainty_score":0.2508738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602075656465953,"score_gpt":0.2922477319949607,"score_spread":0.2762269754303012,"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."}}