{"id":"W2155575944","doi":"10.1109/wvm.1989.47113","title":"On combining points and lines in an image sequence to recover 3D structure and motion","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Rigidity (electromagnetism); Sequence (biology); Motion (physics); Computer science; Computer vision; Image (mathematics); Artificial intelligence; Mathematics; Algorithm; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009395216,0.00006838854,0.00006807208,0.00007706113,0.00004361128,0.0001106888,0.00008466226,0.00001767494,0.00001518219],"category_scores_gemma":[0.00008375426,0.000057122,0.000004082705,0.0001466586,0.00001519092,0.0007727959,0.00004860876,0.00006975734,0.000003056606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001216788,"about_ca_system_score_gemma":0.000006574131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006665562,"about_ca_topic_score_gemma":0.00001330679,"domain_scores_codex":[0.9994389,0.000037054,0.00008326736,0.0002585569,0.00007088419,0.0001112938],"domain_scores_gemma":[0.9997055,0.00003156997,0.00001781189,0.0001511284,0.00002400636,0.00006997162],"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":[0.0000192767,0.00007297131,0.01570563,0.00002085651,0.000002576906,0.00003796776,0.002393264,0.0004554582,0.07916974,0.119362,0.0001707787,0.7825894],"study_design_scores_gemma":[0.002292053,0.0006445146,0.06610667,0.0002110081,0.000002637639,0.0001253365,0.0002939204,0.6902799,0.04379986,0.1943458,0.001069828,0.0008284613],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.585785,0.00001063564,0.4130614,0.0003769183,0.00007267851,0.00006477626,6.476457e-7,0.0000331584,0.0005948464],"genre_scores_gemma":[0.6553819,0.000003892226,0.343307,0.001259325,0.000003409855,5.626572e-7,3.61625e-7,0.000002697521,0.00004084874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.781761,"threshold_uncertainty_score":0.2329368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897781559586502,"score_gpt":0.3017168509890501,"score_spread":0.2827390353931851,"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."}}