{"id":"W4246250782","doi":"10.32920/ryerson.14655897.v1","title":"Adaptive Exposure Fusion for HDR Imaging","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sequence (biology); Fusion; Computer science; Artificial intelligence; Metric (unit); Multiple exposure; Computer vision; Image fusion; Image (mathematics); Pattern recognition (psychology); Engineering","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":[0.0006484585,0.0005565961,0.0004415544,0.0008925331,0.0002806534,0.0007047703,0.0006488041,0.0006157866,0.002613151],"category_scores_gemma":[0.001208097,0.0002600917,0.0006659853,0.0006816747,0.0003741143,0.001085879,0.0007812462,0.0008353832,0.0009056747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003926608,"about_ca_system_score_gemma":0.0002804013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007708648,"about_ca_topic_score_gemma":0.0007623316,"domain_scores_codex":[0.9994541,0.0000790808,0.00002566751,0.0001349207,0.0002684447,0.00003792381],"domain_scores_gemma":[0.9995667,0.0001228345,0.00005647556,0.0001116182,0.0001226845,0.00001955992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003310568,0.00009851892,0.00119628,0.0004295567,0.0001111119,0.0001517969,0.0002256814,0.0244098,0.3033946,0.005747887,0.003091074,0.6608127],"study_design_scores_gemma":[0.00004926925,0.0007682099,0.008911101,0.0001164507,0.0002282265,0.00235559,0.0001802586,0.4455408,0.4864931,0.009911232,0.0453384,0.0001073587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02883569,0.002030056,0.9644871,0.0001217253,0.00006626249,0.00009716353,0.0001258098,0.001349248,0.00288702],"genre_scores_gemma":[0.2760713,0.002169062,0.7166464,0.0001696723,0.0001031534,0.00008064343,0.0004643434,0.0002317558,0.004063772],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002613151,"threshold_uncertainty_score":0.008741856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02304153353440056,"score_gpt":0.2757639509186194,"score_spread":0.2527224173842188,"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."}}