{"id":"W2574199794","doi":"10.1109/ism.2016.0098","title":"Spatio-Temporally Optimized Multi-sensor Motion Fusion","year":2016,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Ground truth; Optical flow; Sensor fusion; Probabilistic logic; Focus (optics); Tracking (education); Motion (physics); Motion estimation; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005885496,0.001041975,0.0008038909,0.0008306333,0.0003030829,0.0006304612,0.000706816,0.0006307831,0.001224604],"category_scores_gemma":[0.001504388,0.0004654143,0.0008547364,0.001090344,0.0003457502,0.001139174,0.0009362057,0.0007420795,0.000369553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005616589,"about_ca_system_score_gemma":0.0008510234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002769781,"about_ca_topic_score_gemma":0.003259983,"domain_scores_codex":[0.9995696,0.00005754328,0.00003409996,0.0001242259,0.0001733337,0.00004111942],"domain_scores_gemma":[0.9996971,0.00006272976,0.0000612768,0.00005872589,0.0001034353,0.00001680726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002119286,0.0000648038,0.001171542,0.00009872744,0.00009861348,0.00009209511,0.00008966914,0.6827515,0.04085742,0.00523253,0.001390383,0.2679408],"study_design_scores_gemma":[0.0000040785,0.00002353456,0.0005899497,0.000004864512,0.000009476667,0.00003464966,0.000008931861,0.9887939,0.007890493,0.001660291,0.0009691778,0.00001074966],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009858645,0.0001580089,0.9889057,0.0000603629,0.00003939406,0.00001712374,0.00006367359,0.0003533944,0.0005437757],"genre_scores_gemma":[0.465787,0.0004449034,0.5295146,0.0001223229,0.00009827169,0.0001094091,0.0005579754,0.000171113,0.003194289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002769781,"threshold_uncertainty_score":0.00550729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03313312033429925,"score_gpt":0.2568248534121959,"score_spread":0.2236917330778966,"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."}}