{"id":"W2890025895","doi":"10.1109/icip.2018.8451488","title":"Image Sharpness Metric Based on Maxpol Convolution Kernels","year":2018,"lang":"en","type":"article","venue":"","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Convolution (computer science); Metric (unit); Pipeline (software); Sensitivity (control systems); Computer science; Artificial intelligence; Noise (video); Focus (optics); Image (mathematics); Image resolution; Kernel (algebra); Range (aeronautics); Pattern recognition (psychology); Computer vision; Algorithm; Mathematics; Optics","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":[0.001710386,0.001054117,0.0007519771,0.00199323,0.0002796684,0.001831377,0.001258839,0.001078371,0.0014897],"category_scores_gemma":[0.005696479,0.0003497334,0.001005698,0.0009555211,0.0008453276,0.002635124,0.001287339,0.0009702727,0.0005884935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001236212,"about_ca_system_score_gemma":0.0004891327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001236829,"about_ca_topic_score_gemma":0.0009387647,"domain_scores_codex":[0.998904,0.0002020362,0.00007679237,0.0002252031,0.0004982454,0.00009377347],"domain_scores_gemma":[0.9975871,0.0006618469,0.0004503407,0.0004954662,0.000699315,0.0001060044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001647326,0.0002562855,0.006257434,0.0009220504,0.0003175624,0.00050271,0.0002445018,0.1859035,0.2934332,0.02276328,0.002898521,0.4848536],"study_design_scores_gemma":[0.00001188683,0.0002926551,0.004869789,0.00003490664,0.00008457334,0.0006373494,0.00002630563,0.8526098,0.1352243,0.004215146,0.001926995,0.00006632884],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05029703,0.000659599,0.9458494,0.00007248081,0.00003422439,0.00006172192,0.0001515555,0.001424048,0.001449958],"genre_scores_gemma":[0.7144883,0.0006561989,0.2820482,0.0001210497,0.00004753793,0.00008580369,0.0005227983,0.0003116922,0.001718483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00199323,"threshold_uncertainty_score":0.009045482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03129690563643266,"score_gpt":0.3244693842049993,"score_spread":0.2931724785685666,"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."}}