{"id":"W2131684180","doi":"10.1109/iros.2013.6696649","title":"RANSAC for motion-distorted 3D visual sensors","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RANSAC; Artificial intelligence; Computer vision; Visual odometry; Computer science; Rolling shutter; Motion estimation; Structure from motion; Bundle adjustment; Feature (linguistics); Shutter; Robot; Photogrammetry; Image (mathematics); Engineering","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":[0.001066028,0.001692927,0.001306165,0.001290406,0.0006467957,0.001263278,0.002499539,0.001329509,0.005283571],"category_scores_gemma":[0.003555409,0.0008645387,0.001619981,0.001554282,0.0008533298,0.001437948,0.001762816,0.001969582,0.003848588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000914449,"about_ca_system_score_gemma":0.001503837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008821759,"about_ca_topic_score_gemma":0.007489129,"domain_scores_codex":[0.9979073,0.0003396872,0.0001126435,0.0003925556,0.00115028,0.00009763089],"domain_scores_gemma":[0.9987568,0.0002002767,0.0001925943,0.0002557448,0.0005593081,0.00003521675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001525424,0.00006336115,0.0008017361,0.0004007769,0.0001445018,0.0002399643,0.000245525,0.4762456,0.01903211,0.03125893,0.01261782,0.4587971],"study_design_scores_gemma":[0.00001173736,0.0000488448,0.0004247984,0.00004310333,0.00001056101,0.0001142927,0.00004138074,0.9706302,0.004735838,0.006873601,0.01702768,0.00003798708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001200459,0.0002089215,0.9963237,0.00004366268,0.00005725378,0.00004841678,0.00009588981,0.001089802,0.0009320041],"genre_scores_gemma":[0.08792188,0.0006583733,0.9032297,0.000180156,0.00009954019,0.0003571961,0.001558624,0.000523649,0.005470948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008821759,"threshold_uncertainty_score":0.01767528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01147752731328713,"score_gpt":0.2781823214783616,"score_spread":0.2667047941650745,"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."}}