{"id":"W2899954294","doi":"10.1007/s11045-018-0621-1","title":"A vehicle detection scheme based on two-dimensional HOG features in the DFT and DCT domains","year":2018,"lang":"en","type":"article","venue":"Multidimensional Systems and Signal Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Discrete cosine transform; Pattern recognition (psychology); Computer science; Classifier (UML); Histogram; Computer vision; Frequency domain; Pixel; Object detection; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003091902,0.0003770392,0.0006664949,0.0009226399,0.0002884994,0.0004525548,0.0005281415,0.0004885405,0.00135149],"category_scores_gemma":[0.0004609133,0.0002349364,0.0003147868,0.0007099339,0.0002418225,0.0005976409,0.0005827647,0.0004438945,0.0009387299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001960768,"about_ca_system_score_gemma":0.0005666342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002031575,"about_ca_topic_score_gemma":0.003484938,"domain_scores_codex":[0.9998248,0.00002032344,0.000009550419,0.00004440572,0.00007285146,0.00002809103],"domain_scores_gemma":[0.9998093,0.00002520182,0.00001264991,0.00003040673,0.00009878878,0.00002368316],"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":[0.0003347726,0.000152561,0.001236713,0.0001033349,0.00005846302,0.00007826411,0.00003875943,0.007933338,0.2177102,0.00376463,0.00276697,0.7658219],"study_design_scores_gemma":[0.0001035822,0.0006818195,0.006933969,0.00003525804,0.0001271771,0.0008039576,0.00005912398,0.8499779,0.1268822,0.002655959,0.01163944,0.00009968226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03401234,0.0004069211,0.9625571,0.0001192077,0.0001938065,0.0001065569,0.000126322,0.0006983307,0.001779389],"genre_scores_gemma":[0.2990297,0.0006242768,0.6913434,0.0001679488,0.0001317848,0.0001077804,0.0006284612,0.00005723675,0.007909318],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002031575,"threshold_uncertainty_score":0.004521132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01946402874976006,"score_gpt":0.2837042297007303,"score_spread":0.2642402009509702,"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."}}