{"id":"W2985767503","doi":"10.1109/tim.2019.2953435","title":"Scattering Key-Frame Extraction for Comprehensive VideoSAR Summarization: A Spatiotemporal Background Subtraction Perspective","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; Aeronautical Science Foundation of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Key frame; Automatic summarization; Artificial intelligence; Frame (networking); Computer vision; Key (lock); Background subtraction; Scattering; Feature extraction; Pattern recognition (psychology); Optics; Pixel; Physics","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":[0.0001260601,0.0002072077,0.0001647497,0.0002041598,0.0001583024,0.00007930877,0.00005018594,0.00007784032,0.00005349718],"category_scores_gemma":[0.000002570584,0.0002431523,0.00006676754,0.0001387753,0.00003367975,0.0006575017,7.721319e-7,0.0001846947,0.00002039154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000767234,"about_ca_system_score_gemma":0.00002512274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005050283,"about_ca_topic_score_gemma":0.00004736535,"domain_scores_codex":[0.9989093,0.00002884582,0.0002687875,0.0002846058,0.0003214391,0.0001870566],"domain_scores_gemma":[0.9993985,0.00003665784,0.00007344781,0.0001623314,0.0002644599,0.00006465612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003108276,0.0002013629,0.0002247435,0.000347709,0.000248825,9.8108e-7,0.00134279,0.06224221,0.8689965,0.0007795016,0.0004134302,0.0648911],"study_design_scores_gemma":[0.003818948,0.0005987979,0.003343997,0.0003476428,0.0001674257,0.00004168399,0.006580948,0.08656265,0.8880777,0.001456408,0.008027987,0.0009758628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1140687,0.00004910743,0.8826311,0.0002481727,0.001034819,0.0009947667,0.0000280229,0.0005657221,0.0003796237],"genre_scores_gemma":[0.9843522,0.0001131531,0.0150622,0.0001168091,0.00004345337,0.0002078491,0.00001795361,0.00004519818,0.00004116293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8702835,"threshold_uncertainty_score":0.9915463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05164810870132325,"score_gpt":0.3000438968619311,"score_spread":0.2483957881606079,"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."}}