{"id":"W2805794463","doi":"10.3390/s19061409","title":"Construction of All-in-Focus Images Assisted by Depth Sensing","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China; Science and Technology Commission of Shanghai Municipality; Natural Science Foundation of Shanghai","keywords":"Focus (optics); Artificial intelligence; Computer vision; Computer science; Image fusion; Depth of field; RGB color model; Segmentation; Image (mathematics)","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":[0.0004430533,0.001158972,0.0006914548,0.00168391,0.0003321432,0.000878262,0.001014572,0.0008140858,0.001496626],"category_scores_gemma":[0.00105496,0.0005080652,0.0008382617,0.0009030754,0.0004120028,0.001589229,0.001139832,0.000866672,0.0005721328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004130992,"about_ca_system_score_gemma":0.0005495203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001310492,"about_ca_topic_score_gemma":0.001911865,"domain_scores_codex":[0.9995629,0.00003797644,0.00001663598,0.00009391635,0.0002406527,0.00004777125],"domain_scores_gemma":[0.9994566,0.00008928593,0.00007789231,0.0001018349,0.0002450478,0.00002932403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003199406,0.00009327902,0.001127857,0.0005902234,0.0001062197,0.0002934472,0.0003528461,0.03335395,0.5586262,0.004872595,0.00262016,0.3976432],"study_design_scores_gemma":[0.00003568712,0.0002473091,0.00378061,0.00004608621,0.0001085182,0.0009229689,0.0001889403,0.3842549,0.5957281,0.003944541,0.01064426,0.00009804262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03570318,0.0005011199,0.9600559,0.0001016667,0.00006639269,0.00009718544,0.0001511498,0.001152448,0.002170784],"genre_scores_gemma":[0.1897904,0.0006584863,0.8072835,0.00009893524,0.00003533864,0.00006652321,0.0003351657,0.0002396932,0.001491996],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00168391,"threshold_uncertainty_score":0.005006671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01068741968573054,"score_gpt":0.2446042167774106,"score_spread":0.2339167970916801,"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."}}