{"id":"W2163164795","doi":"10.1109/icpr.2008.4761007","title":"Structure from Motion: Combining features correspondences and optical flow","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Optical flow; Feature (linguistics); Probabilistic logic; Artificial intelligence; Computer vision; Computer science; Motion (physics); Motion estimation; Perspective (graphical); Flow (mathematics); Monte Carlo method; Structure from motion; Pattern recognition (psychology); Mathematics; Algorithm; Image (mathematics); Geometry; Statistics","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":[0.001344118,0.001193707,0.00138138,0.003758005,0.0005369497,0.001543186,0.001105738,0.001536326,0.00138621],"category_scores_gemma":[0.005424296,0.0009060826,0.0009987287,0.003111962,0.0009995393,0.004542667,0.002242841,0.001051481,0.0005571162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005316262,"about_ca_system_score_gemma":0.0008172402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002304513,"about_ca_topic_score_gemma":0.002278324,"domain_scores_codex":[0.9990102,0.000239894,0.00003411417,0.0002372064,0.0003949236,0.00008371723],"domain_scores_gemma":[0.9989554,0.000394663,0.0002373205,0.0001474763,0.0002008989,0.00006420037],"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":[0.0001681058,0.00009756909,0.001974211,0.0002570291,0.0001781967,0.0002306971,0.0002409554,0.2624568,0.01944392,0.06476326,0.00275176,0.6474375],"study_design_scores_gemma":[0.00003161947,0.0001548322,0.001948508,0.00005405434,0.00006441415,0.0002150858,0.00006169215,0.8935758,0.005107867,0.09347343,0.005256455,0.00005624285],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005954264,0.0005385067,0.992156,0.0001330455,0.00006502827,0.00002998794,0.00003359928,0.0002184854,0.0008710321],"genre_scores_gemma":[0.3998504,0.001833892,0.5942582,0.0001868122,0.000530068,0.0001552001,0.0003382381,0.0002157088,0.00263151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003758005,"threshold_uncertainty_score":0.00710845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06885609883373368,"score_gpt":0.297112631449534,"score_spread":0.2282565326158003,"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."}}