{"id":"W2293693209","doi":"10.1007/978-3-319-27857-5_25","title":"UT-MARO: Unscented Transformation and Matrix Rank Optimization for Moving Objects Detection in Aerial Imagery","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Aerial image; Robustness (evolution); Artificial intelligence; Computer vision; Computation; Object detection; Aerial imagery; Transformation (genetics); Transformation matrix; Pattern recognition (psychology); Image (mathematics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006680695,0.001444393,0.00135095,0.000675386,0.0003438405,0.001028754,0.00142381,0.001121508,0.007711302],"category_scores_gemma":[0.002370687,0.0006089958,0.001033029,0.001105987,0.0006086288,0.001303506,0.001537975,0.001848572,0.004462879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003359687,"about_ca_system_score_gemma":0.0008241102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002823135,"about_ca_topic_score_gemma":0.004457789,"domain_scores_codex":[0.9993786,0.0001365407,0.00002938598,0.0001187398,0.0002874648,0.00004927759],"domain_scores_gemma":[0.9995111,0.0001786282,0.00003588257,0.000105443,0.0001427995,0.00002625666],"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":[0.0001980998,0.00008981182,0.0001923127,0.0003140024,0.00008171277,0.00007880168,0.00008219382,0.09179164,0.01920749,0.02122715,0.04136349,0.8253734],"study_design_scores_gemma":[0.0000168055,0.00006075352,0.0002141289,0.00002221285,0.00001023427,0.00008609983,0.00001617635,0.9678075,0.006665019,0.01138814,0.01369217,0.0000208588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001041887,0.0003049362,0.99591,0.00006684245,0.00008966356,0.00002456044,0.0001420769,0.001444211,0.0009757856],"genre_scores_gemma":[0.02159211,0.0004224857,0.9697971,0.00009515655,0.0001210805,0.0001282287,0.0008762688,0.0007643824,0.006203238],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007711302,"threshold_uncertainty_score":0.02579683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225866325838672,"score_gpt":0.2855284726706321,"score_spread":0.2629418400867649,"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."}}