{"id":"W2113282228","doi":"10.1109/isvd.2012.8","title":"Improving RANSAC Feature Matching with Local Topological Information","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"RANSAC; Outlier; Robustness (evolution); Delaunay triangulation; Artificial intelligence; Computer science; Computer vision; Mathematics; Matching (statistics); Pattern recognition (psychology); Noise (video); Image (mathematics); Algorithm; Statistics","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":[0.00337526,0.002095949,0.002337509,0.003660829,0.0009604961,0.001347386,0.002458494,0.001982415,0.003851695],"category_scores_gemma":[0.01593373,0.001059012,0.00184026,0.003758476,0.0009873722,0.003005812,0.001954192,0.00178313,0.003071615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007266066,"about_ca_system_score_gemma":0.001313733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007391999,"about_ca_topic_score_gemma":0.005304312,"domain_scores_codex":[0.9951251,0.0009716678,0.0002549005,0.0006821132,0.002700781,0.0002654274],"domain_scores_gemma":[0.9918445,0.001889117,0.0008341902,0.001843868,0.003476768,0.0001116513],"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":[0.0002169325,0.0001954937,0.001602751,0.0001829475,0.0001716372,0.000153325,0.0002376035,0.200807,0.02872451,0.006798546,0.004322313,0.756587],"study_design_scores_gemma":[0.00002104305,0.0001059414,0.0007555063,0.00001266852,0.00003256683,0.0001323841,0.00005022506,0.982821,0.01132927,0.001973455,0.002727876,0.00003804635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00700566,0.0001344065,0.9912437,0.00003702071,0.00003881606,0.00004395254,0.00002354805,0.00101085,0.0004621329],"genre_scores_gemma":[0.1234482,0.0002581229,0.8735799,0.00009179989,0.00007351411,0.0001527654,0.0003959205,0.0003905912,0.001609182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007391999,"threshold_uncertainty_score":0.01785028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007234247486561852,"score_gpt":0.2397898658542975,"score_spread":0.2325556183677357,"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."}}