{"id":"W2774346682","doi":"10.15353/vsnl.v3i1.165","title":"Depth from Defocus via Active Multispectral Quasi-random Point Projections using Deep Learning","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial intelligence; Multispectral image; RGB color model; Projection (relational algebra); Point (geometry); Computer vision; Computer science; Depth map; Deep learning; Pattern recognition (psychology); Image (mathematics); Mathematics; Algorithm; Geometry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0002159271,0.00012516,0.0002253613,0.0001359089,0.0005560175,0.0004600789,0.0001409703,0.00003362279,0.000003707123],"category_scores_gemma":[0.00005095739,0.0001083399,0.00007148361,0.00004970613,0.00005616262,0.0006282983,0.00002930788,0.0002762435,0.000001913506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000817147,"about_ca_system_score_gemma":0.00002920995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001448421,"about_ca_topic_score_gemma":0.000003819227,"domain_scores_codex":[0.9991542,0.00003958488,0.0003763164,0.00009747584,0.0002069271,0.0001254785],"domain_scores_gemma":[0.9991207,0.0001007163,0.0003672117,0.00009525131,0.0002438552,0.0000723067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001067903,0.0001700232,0.00722606,0.000139688,0.0002321878,0.00004049813,0.00221632,0.6901867,0.02202112,0.0003640994,0.000413407,0.2768831],"study_design_scores_gemma":[0.0006725928,0.00003028447,0.009793823,0.0002451728,0.0000325681,0.0003243139,0.0002840949,0.9857164,0.0003825295,0.002010882,0.0003808203,0.0001265261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1112109,0.0008774568,0.8870886,0.0001140944,0.0002758403,0.0001122582,0.000003638821,0.00007429889,0.0002428856],"genre_scores_gemma":[0.9593176,0.00003252858,0.04038551,0.000009158664,0.0002172744,0.000003833903,0.000003853578,0.00002161525,0.00000862696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8481067,"threshold_uncertainty_score":0.4436551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01223774229803855,"score_gpt":0.2949107335159443,"score_spread":0.2826729912179058,"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."}}