{"id":"W2113598111","doi":"10.1016/j.jvcir.2011.06.003","title":"Efficient video sequences alignment using unbiased bidirectional dynamic time warping","year":2011,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Dynamic time warping; Image warping; Computer vision; Frame (networking); Artificial intelligence; Sequence (biology); Resolution (logic); Reference frame; Algorithm","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":[0.0006049832,0.0000932798,0.0001496152,0.000215485,0.0002208166,0.0001248171,0.0003574005,0.00002493776,0.00003089431],"category_scores_gemma":[0.0001199987,0.00008355542,0.00006334789,0.0002847338,0.00009498739,0.0008484069,0.0001753378,0.0001448059,0.000007129115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007905177,"about_ca_system_score_gemma":0.00005693476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000293114,"about_ca_topic_score_gemma":6.101706e-7,"domain_scores_codex":[0.9987128,0.0002705358,0.0004487224,0.0001454809,0.0003077774,0.0001147065],"domain_scores_gemma":[0.9986876,0.0001314627,0.0005228956,0.0002735656,0.0003032441,0.00008125334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001462786,0.0008401757,0.001645964,0.00003608699,0.0001720077,0.00002953875,0.009094329,0.003906874,0.7880781,0.001632923,0.0002169364,0.1942008],"study_design_scores_gemma":[0.0004629049,0.0001060005,0.004523343,0.0001084234,0.0000197163,0.0001823119,0.0005027899,0.9779624,0.01479015,0.001145288,0.00007800426,0.0001187043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2262926,0.0007013843,0.7716911,0.0003866863,0.0001246703,0.00009668797,5.220994e-7,0.00003223493,0.0006740532],"genre_scores_gemma":[0.7314029,0.0002241217,0.268242,0.00008753518,0.00001459095,0.00000110482,0.000001534768,0.000005504242,0.00002062307],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9740555,"threshold_uncertainty_score":0.3407291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04974990218697384,"score_gpt":0.375172297094229,"score_spread":0.3254223949072552,"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."}}