{"id":"W2053747117","doi":"10.1109/icdsp.2013.6622827","title":"Edge-aware temporally consistent SimpleFlow: Optical flow without global optimization","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Optical flow; Computer science; Enhanced Data Rates for GSM Evolution; Computer vision; Pixel; Artificial intelligence; Video tracking; Consistency (knowledge bases); Motion estimation; Tracking (education); Data compression; Object (grammar); Image (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009574797,0.0001512018,0.000158698,0.00003482094,0.000113376,0.0003202368,0.0004222453,0.00004466632,0.0004056456],"category_scores_gemma":[0.00008573223,0.0001211771,0.00006000275,0.0002530987,0.00005343274,0.001008976,0.0002226196,0.00007810922,0.0003640103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006086308,"about_ca_system_score_gemma":0.00009014142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001292428,"about_ca_topic_score_gemma":0.000002629026,"domain_scores_codex":[0.9987515,0.00003830692,0.00025176,0.000386391,0.000266886,0.0003051325],"domain_scores_gemma":[0.9990337,0.00003501135,0.00005205107,0.000449879,0.0002187823,0.000210613],"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":[0.00001121149,0.0003371459,0.00925089,0.00003535065,0.00005134839,0.00003838198,0.0001597202,0.1847315,0.0003519395,0.2623186,0.01653002,0.5261838],"study_design_scores_gemma":[0.0003249577,0.00003586215,0.0006632946,0.00001235292,0.000002559733,0.00002981656,0.00002910923,0.9934872,0.0002455383,0.001992719,0.002990011,0.0001865345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002931289,0.00002526664,0.9793761,0.001352019,0.0002513683,0.0002189284,0.000001020652,0.0003521621,0.01812999],"genre_scores_gemma":[0.2768612,0.000004138781,0.7216459,0.001062429,0.00002447645,0.00001098914,0.000003244122,0.00000599648,0.0003816359],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8087557,"threshold_uncertainty_score":0.4941459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01313932380512759,"score_gpt":0.2688413896876541,"score_spread":0.2557020658825265,"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."}}