{"id":"W1524530684","doi":"10.1109/crv.2015.41","title":"Preprocessing Realistic Video for Contactless Heart Rate Monitoring Using Video Magnification","year":2015,"lang":"en","type":"article","venue":"","topic":"Image and Video Stabilization","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Magnification; Computer science; Computer vision; Artificial intelligence; Eulerian path; Preprocessor; 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":[0.0002058019,0.0003157333,0.0002319521,0.0004767335,0.0001594527,0.000469051,0.0002953545,0.0003245358,0.00320775],"category_scores_gemma":[0.001294192,0.0001162392,0.0002087352,0.0003059422,0.0001257678,0.0004267193,0.0002355932,0.0002686895,0.0006643963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002085867,"about_ca_system_score_gemma":0.0002436021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007840635,"about_ca_topic_score_gemma":0.001042137,"domain_scores_codex":[0.9998519,0.00002151766,0.000009084981,0.00003629583,0.00006590867,0.00001524629],"domain_scores_gemma":[0.9997506,0.00008908064,0.00003520073,0.00003930185,0.0000720372,0.00001387523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004848528,0.00008220934,0.002191832,0.0003049083,0.00002568091,0.0003522161,0.0001694601,0.005428242,0.6889185,0.001424292,0.001835029,0.2987828],"study_design_scores_gemma":[0.000116457,0.001558465,0.04397798,0.0001368304,0.000135733,0.002929465,0.0003451736,0.2753433,0.6430591,0.002272877,0.03003556,0.00008913918],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1961064,0.0005137927,0.7965789,0.000215557,0.0001426281,0.000310495,0.0004631178,0.001756704,0.003912447],"genre_scores_gemma":[0.6005402,0.00116968,0.3929042,0.0001549746,0.0001352824,0.0001828979,0.00090513,0.0002001309,0.003807514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00320775,"threshold_uncertainty_score":0.01073098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1230170583174801,"score_gpt":0.3509241706939416,"score_spread":0.2279071123764615,"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."}}