{"id":"W2561860101","doi":"10.1007/s11432-016-0037-0","title":"High-speed visual target tracking with mixed rotation invariant description and skipping searching","year":2016,"lang":"en","type":"article","venue":"Science China Information Sciences","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computation; Computer vision; Computer science; Artificial intelligence; Invariant (physics); Histogram; Eye tracking; Rotation (mathematics); Tracking system; Pixel; Tracking (education); Speedup; Algorithm; 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":[0.0005557929,0.0007997547,0.001457844,0.0007625943,0.0005173181,0.0007544578,0.001355751,0.0008572715,0.001164358],"category_scores_gemma":[0.001273722,0.0005475602,0.0007130203,0.00136766,0.0002878617,0.00169302,0.001216855,0.0007085692,0.000607626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003279674,"about_ca_system_score_gemma":0.001025685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003538893,"about_ca_topic_score_gemma":0.003655049,"domain_scores_codex":[0.9995582,0.00006939018,0.00003253897,0.0001149101,0.0001681751,0.0000568385],"domain_scores_gemma":[0.9994909,0.000143739,0.00006645743,0.0001410208,0.0001194254,0.00003854806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009288029,0.000239581,0.001949146,0.0002545699,0.000289219,0.000265898,0.000110226,0.1995345,0.0794981,0.01349336,0.004384715,0.699052],"study_design_scores_gemma":[0.00001778626,0.00007221023,0.0003848664,0.00000485936,0.00002608974,0.0001330556,0.000006872941,0.9902574,0.006186517,0.002179886,0.0007139905,0.00001649738],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02539617,0.0003946658,0.9725904,0.00005068544,0.00003776794,0.0000245937,0.00007717883,0.0006217124,0.0008066772],"genre_scores_gemma":[0.5822558,0.0004451719,0.4098086,0.0002169344,0.00006763915,0.0001114498,0.0008990498,0.0001715577,0.006023787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003538893,"threshold_uncertainty_score":0.007036626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01928511871377777,"score_gpt":0.2847301898675682,"score_spread":0.2654450711537905,"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."}}