{"id":"W2947190698","doi":"10.1002/sdtp.13190","title":"P‐35: Human Visual System Inspired Artifact Reduction in Projector Compensation","year":2019,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Christie (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Projector; Computer vision; Computer science; Artificial intelligence; Brightness; Luminance; Projection (relational algebra); Compensation (psychology); High dynamic range; Contrast (vision); Dynamic range; Optics; Physics; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004404664,0.0001951288,0.000348357,0.0002348986,0.0000490062,0.00006713827,0.0006381313,0.0001736726,0.00001687397],"category_scores_gemma":[0.00002974193,0.0001741608,0.0001070963,0.0004448652,0.00009326233,0.0004817324,0.0001583866,0.0002468317,0.0000380738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003145643,"about_ca_system_score_gemma":0.00005687762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008164606,"about_ca_topic_score_gemma":0.00002269903,"domain_scores_codex":[0.998062,0.00009280026,0.0005978662,0.0004753055,0.0004723457,0.0002997145],"domain_scores_gemma":[0.9991016,0.00004020566,0.0001920773,0.0005000986,0.00009257801,0.0000733983],"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":[0.00002079655,0.0001608756,0.003543299,0.00007360693,0.000005686906,0.00000130386,0.00006385281,0.00001623367,0.9663048,0.02968905,0.00001266849,0.000107844],"study_design_scores_gemma":[0.0007432433,0.002233234,0.05998804,0.0009024341,0.00001560107,0.00001822313,0.0001479964,0.0004095831,0.9346233,0.0001578948,0.000211536,0.0005488955],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6524996,0.00001110523,0.0000520154,0.0003991635,0.0002644162,0.0008415714,9.974945e-7,0.0006592618,0.3452719],"genre_scores_gemma":[0.9994224,0.000004797086,0.0003754581,0.00002014879,0.00003328683,0.00005492008,0.000005290647,0.0000134863,0.00007022582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3469228,"threshold_uncertainty_score":0.7102072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01664697294105748,"score_gpt":0.2667889636245094,"score_spread":0.2501419906834519,"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."}}