{"id":"W2807438111","doi":"10.1109/tcsvt.2018.2843761","title":"KRMARO: Aerial Detection of Small-Size Ground Moving Objects Using Kinematic Regularization and Matrix Rank Optimization","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Object detection; Regularization (linguistics); Artificial intelligence; Robust principal component analysis; Computer vision; Robustness (evolution); Computer science; Kinematics; Aerial image; Mathematics; Pattern recognition (psychology); Principal component analysis; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007454772,0.001137268,0.001002152,0.0009065251,0.0003387737,0.0006881288,0.001117488,0.0008074463,0.001236607],"category_scores_gemma":[0.002247411,0.0004806698,0.0007150535,0.0006942675,0.0006758396,0.0009680884,0.001162422,0.001030105,0.0007055512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004021594,"about_ca_system_score_gemma":0.001049559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004366451,"about_ca_topic_score_gemma":0.006137871,"domain_scores_codex":[0.9993719,0.0001260549,0.0000248401,0.0001325979,0.0002819039,0.00006264904],"domain_scores_gemma":[0.9994544,0.0002011239,0.0001104429,0.00007702999,0.0001248023,0.00003218808],"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":[0.0002644109,0.0001283474,0.001536199,0.0002297317,0.0001387173,0.0001786637,0.00015256,0.3981901,0.04445935,0.01419522,0.008482318,0.5320444],"study_design_scores_gemma":[0.00001055698,0.00003815441,0.0003734362,0.000006519318,0.000008110482,0.00005547738,0.000013632,0.9921175,0.003811615,0.001751064,0.001800808,0.00001328847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005994129,0.0001341364,0.9918954,0.0000919058,0.00002451926,0.00003480761,0.00004788457,0.0008903765,0.0008869208],"genre_scores_gemma":[0.1269096,0.0002410962,0.8686853,0.0001371912,0.00005954061,0.0001110609,0.0005138486,0.0002750023,0.003067381],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004366451,"threshold_uncertainty_score":0.008682072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01566769434984194,"score_gpt":0.2246021758803946,"score_spread":0.2089344815305526,"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."}}