{"id":"W2538968370","doi":"10.1016/j.jvcir.2016.10.008","title":"Gaussian-Hermite moment-based depth estimation from single still image for stereo vision","year":2016,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Artificial intelligence; Depth map; Pixel; Focus (optics); Rendering (computer graphics); Computer science; Gaussian; Binocular disparity; Mathematics; Stereopsis; Computer stereo vision; Laplace operator; Image (mathematics); Optics","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":[0.0004721568,0.0001324556,0.0002062606,0.0002044631,0.0001498266,0.0003319308,0.0004510361,0.0000541831,0.0000172912],"category_scores_gemma":[0.0003355734,0.00009646708,0.00009528935,0.0001911578,0.00009552478,0.003482097,0.0001407975,0.0001357521,0.000007350664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008301611,"about_ca_system_score_gemma":0.00004866048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009478402,"about_ca_topic_score_gemma":0.000002666775,"domain_scores_codex":[0.9984937,0.0002469295,0.0005847743,0.0002129718,0.0003143515,0.0001472139],"domain_scores_gemma":[0.9974889,0.0006670152,0.0007566274,0.0004898164,0.0004861127,0.0001115123],"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":[0.0000937043,0.0001961538,0.0002137769,0.000011224,0.00001446705,0.000001597165,0.0002870121,0.00002813522,0.344425,0.0002559562,0.0005353032,0.6539377],"study_design_scores_gemma":[0.005701424,0.0009319976,0.01548611,0.0006314237,0.00004781015,0.00002893638,0.0003387338,0.756706,0.2082122,0.007980787,0.00358239,0.0003521673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02014551,0.0002588867,0.9737799,0.005261607,0.000109419,0.0002020514,0.000005199934,0.00003753729,0.0001999077],"genre_scores_gemma":[0.5100803,0.0001568586,0.489451,0.0002018225,0.00003317878,0.000005834778,0.00001226806,0.00001056756,0.00004818002],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7566779,"threshold_uncertainty_score":0.3933813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03319345638644171,"score_gpt":0.3859866673868233,"score_spread":0.3527932110003816,"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."}}