{"id":"W364310421","doi":"10.1007/978-3-319-00711-3","title":"Computer Vision Analysis of Image Motion by Variational Methods","year":2013,"lang":"en","type":"book","venue":"Springer topics in signal processing","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Motion (physics); Image (mathematics); Statistic; Computer science; Computer vision; Motion analysis; Artificial intelligence; Variational method; Variational analysis; Calculus (dental); Classical mechanics; Mathematics; Computer graphics (images); Applied mathematics; Physics; Mathematical analysis; Statistics","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":[0.000460289,0.0007486965,0.001014507,0.001120425,0.0002088289,0.001182247,0.001333008,0.0008769605,0.004642247],"category_scores_gemma":[0.001247423,0.0006768042,0.001024427,0.001357432,0.0007439932,0.001035713,0.0009129416,0.001493378,0.001587322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006642168,"about_ca_system_score_gemma":0.0005931748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004664527,"about_ca_topic_score_gemma":0.004071895,"domain_scores_codex":[0.9998036,0.00003659995,0.000009176317,0.00004674085,0.00008778633,0.00001610816],"domain_scores_gemma":[0.9997615,0.0001175888,0.00001787056,0.00003507351,0.00005483593,0.00001306628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000384144,0.00003187118,0.0002633626,0.0004839246,0.0001844758,0.00007029592,0.0001134898,0.295416,0.0184107,0.2034931,0.02159466,0.4598997],"study_design_scores_gemma":[0.000007489061,0.00001103313,0.0002647807,0.00002824386,0.00001993603,0.00008526923,0.00001159036,0.8809411,0.002179174,0.09845635,0.01797916,0.00001594468],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008201005,0.001866977,0.9943468,0.0001659396,0.0001014037,0.0000123581,0.00005273069,0.0001685843,0.002465004],"genre_scores_gemma":[0.0999224,0.00921957,0.8544973,0.0002296583,0.00056502,0.000130989,0.0006359122,0.0007091928,0.03409],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004664527,"threshold_uncertainty_score":0.01552987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01677894488475118,"score_gpt":0.3357113824124073,"score_spread":0.3189324375276561,"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."}}