{"id":"W2889771635","doi":"10.1016/j.optcom.2018.08.064","title":"Optimized stereo matching algorithm for integral imaging microscopy and its potential use in precise 3-D optical manipulation","year":2018,"lang":"en","type":"article","venue":"Optics Communications","topic":"Digital Holography and Microscopy","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Integral imaging; Microlens; Optics; Interpolation (computer graphics); Optical tweezers; Computer science; Lens (geology); Microscopy; Matching (statistics); Algorithm; Materials science; Computer vision; Image (mathematics); Physics; 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":[0.0005933238,0.0004154654,0.0006011832,0.0008829003,0.0004017025,0.0007388185,0.001134148,0.0008180724,0.004008404],"category_scores_gemma":[0.001501392,0.0003234522,0.0005235356,0.001215368,0.0003143277,0.0009480791,0.0008341427,0.0006581327,0.001100487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005186378,"about_ca_system_score_gemma":0.001848742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002976354,"about_ca_topic_score_gemma":0.003513463,"domain_scores_codex":[0.9995053,0.00006759618,0.00002719636,0.00006961518,0.0002910447,0.00003921157],"domain_scores_gemma":[0.9994121,0.0001198064,0.00006808114,0.0001027095,0.00027134,0.00002600283],"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":[0.0004120535,0.0001897802,0.001232142,0.0002054337,0.0000787591,0.00009927536,0.0001617855,0.1241754,0.1223615,0.03665246,0.005281898,0.7091495],"study_design_scores_gemma":[0.00001305318,0.00004936879,0.0004177262,0.000007304446,0.00001245954,0.0001184707,0.00001510439,0.97296,0.02029692,0.00326827,0.002823052,0.00001833237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005812098,0.00005626852,0.9930058,0.00002916067,0.00002060254,0.00001960727,0.00003686901,0.0003051974,0.0007144552],"genre_scores_gemma":[0.07122234,0.00009876149,0.9263136,0.00005396469,0.00001863898,0.00006910027,0.0001860894,0.0001586784,0.001878848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004008404,"threshold_uncertainty_score":0.0134095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02706977587765115,"score_gpt":0.3114944778181105,"score_spread":0.2844247019404593,"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."}}