{"id":"W2899246346","doi":"10.1109/tsmc.2018.2872891","title":"Feature Selection Based on Tensor Decomposition and Object Proposal for Night-Time Multiclass Vehicle Detection","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China; City University of Hong Kong","keywords":"Computer science; Histogram; Artificial intelligence; Feature (linguistics); False positive paradox; Object detection; Pattern recognition (psychology); Histogram of oriented gradients; Computer vision; Feature extraction; Local binary patterns; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002278174,0.0002784407,0.0002785808,0.0002131795,0.0008196589,0.0004137567,0.000182017,0.0001952803,9.220399e-7],"category_scores_gemma":[0.000004165201,0.0002554026,0.00006529631,0.0003884707,0.0001101492,0.0002256096,0.000003031219,0.0002107153,0.00002779187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001375514,"about_ca_system_score_gemma":0.00004501162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004998778,"about_ca_topic_score_gemma":0.00007281994,"domain_scores_codex":[0.9982368,0.0001817997,0.0003001082,0.0006712357,0.0002835005,0.0003265598],"domain_scores_gemma":[0.9988012,0.0002411878,0.0001762134,0.0003866179,0.0002372158,0.000157566],"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":[0.001371223,0.001375432,0.0001851649,0.00132362,0.0003693662,0.00001278918,0.001707335,0.3960439,0.498604,0.01566717,0.002359102,0.08098089],"study_design_scores_gemma":[0.0008541643,0.001145491,0.0001391519,0.0001471505,0.00003593088,0.00008440472,0.00003824592,0.9685456,0.02706376,0.00007026087,0.001590581,0.0002852376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0341421,0.00006582036,0.9621488,0.0003841906,0.0008919084,0.001893637,0.00002594711,0.000333482,0.0001140779],"genre_scores_gemma":[0.9919897,0.000008954517,0.006046921,0.00007231533,0.00028008,0.0005528348,0.000003384348,0.00003752493,0.001008306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9578476,"threshold_uncertainty_score":0.9999898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008965563727785557,"score_gpt":0.2422559370176122,"score_spread":0.2332903732898267,"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."}}