{"id":"W2099570353","doi":"10.1109/crv.2011.51","title":"Extending Filter-based Structure from Motion to Large Baselines","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Filter (signal processing); Computer vision; Structure from motion; Motion (physics); State vector; Process (computing); Motion vector; Probabilistic logic; Data set; Displacement (psychology); Set (abstract data type); Motion estimation; Pattern recognition (psychology); 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":[0.00105124,0.0007067409,0.0009158297,0.001111176,0.0006126986,0.0007851151,0.001092772,0.001532875,0.001515254],"category_scores_gemma":[0.004242657,0.0006366088,0.0008560463,0.001279473,0.0005932822,0.002611374,0.001254942,0.001285117,0.0005880917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008394576,"about_ca_system_score_gemma":0.001089025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01111742,"about_ca_topic_score_gemma":0.00960526,"domain_scores_codex":[0.9994885,0.00007432356,0.00002244727,0.0001520469,0.0002072968,0.00005545663],"domain_scores_gemma":[0.9988146,0.0003860596,0.0001780623,0.0002980109,0.0002694282,0.00005390903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001615819,0.0001039259,0.002553843,0.0001130991,0.0001294077,0.0002428002,0.0002630798,0.5883648,0.03106238,0.05345341,0.002848265,0.3207034],"study_design_scores_gemma":[0.000008203916,0.00003241239,0.0005015633,0.000006643354,0.00000746498,0.00005050897,0.000006514947,0.9810816,0.001518032,0.01494871,0.001823629,0.00001467821],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008479918,0.0001658582,0.9900449,0.0000855754,0.00002709245,0.0000138995,0.00006672975,0.0003776282,0.0007383827],"genre_scores_gemma":[0.3716041,0.0006760703,0.6233675,0.0001601249,0.0001650824,0.00008845249,0.0004946733,0.0003141974,0.003129861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01111742,"threshold_uncertainty_score":0.02210546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03211617229500792,"score_gpt":0.2714171705811462,"score_spread":0.2393009982861383,"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."}}