{"id":"W2565929657","doi":"10.1109/crv.2016.62","title":"Blur Calibration for Depth from Defocus","year":2016,"lang":"en","type":"article","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Gaussian blur; Kernel (algebra); Artificial intelligence; Computer vision; Image restoration; Computer science; Calibration; Pixel; Mathematics; Kernel density estimation; Aperture (computer memory); Image (mathematics); Image processing; Physics; Statistics","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":[0.00001481045,0.00003691898,0.00003166979,0.00001088114,0.0000214868,0.00001560401,0.00004692879,0.00002522954,0.00005039171],"category_scores_gemma":[0.000005191027,0.00002406493,0.00001461257,0.00002459895,0.000005806936,0.00008417018,0.000004930858,0.00001054929,0.00001450038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001108746,"about_ca_system_score_gemma":0.000003514339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007457154,"about_ca_topic_score_gemma":0.00001476496,"domain_scores_codex":[0.9998022,9.108269e-7,0.00005621868,0.00005674786,0.00002101288,0.0000629046],"domain_scores_gemma":[0.9998601,0.00002421594,0.000005563893,0.00008212892,0.00001208786,0.00001587943],"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":[0.000004658009,0.00001952857,0.0005079962,0.00002463522,0.00001786825,3.112714e-7,0.00005080923,0.00007629582,0.4327325,0.02009763,0.07607593,0.4703918],"study_design_scores_gemma":[0.0001872784,0.000008612055,0.0002197306,0.0000166676,0.000007301886,5.586259e-7,0.000004281982,0.05080834,0.8647375,0.04435619,0.03952502,0.0001284995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004516159,0.00004152083,0.9906816,0.000364498,0.000021848,0.0000858844,0.0000145198,0.0007013286,0.003572643],"genre_scores_gemma":[0.848286,0.0000102006,0.1510008,0.00004164269,0.0000599029,0.0001131405,0.000006956476,0.0000132682,0.0004681213],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8437698,"threshold_uncertainty_score":0.09813393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01405024577719286,"score_gpt":0.2389158472550923,"score_spread":0.2248656014778994,"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."}}