{"id":"W1504883860","doi":"10.1007/978-3-540-89646-3_20","title":"Combining Line and Point Correspondences for Homography Estimation","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"U.S. Consumer Product Safety Commission","keywords":"Normalization (sociology); Computer science; Homography; Artificial intelligence; Line (geometry); Computer vision; Point (geometry); Algorithm; Line segment; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005313999,0.0003682463,0.0003992383,0.0009034316,0.0004091489,0.0003942182,0.00132597,0.0001400897,0.00000327837],"category_scores_gemma":[0.0001539072,0.0003324972,0.00009709732,0.000477966,0.0007013087,0.0009583107,0.0006576242,0.000405855,0.000005854696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006632595,"about_ca_system_score_gemma":0.0002052824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003567237,"about_ca_topic_score_gemma":0.000006356768,"domain_scores_codex":[0.9974635,0.00001731038,0.0004048447,0.001131264,0.0005458485,0.0004372496],"domain_scores_gemma":[0.9980239,0.0007298652,0.0002554755,0.0006400432,0.0002090152,0.0001416476],"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":[0.000007083729,0.00001140858,0.00001753268,0.00002112686,0.000003494026,0.00001822336,0.0006514654,0.01233338,0.00004928049,0.004263391,0.00003440183,0.9825892],"study_design_scores_gemma":[0.0002942527,0.0002225793,0.00005866952,0.0003270705,0.000003509452,0.0001109914,2.199651e-7,0.8826279,0.0005730991,0.1139528,0.001454526,0.0003743771],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004776573,0.001108226,0.9959984,0.0008032253,0.001056556,0.0003896784,0.000003566868,0.0001432468,0.0004493489],"genre_scores_gemma":[0.03996211,0.0001988657,0.9582039,0.001308889,0.0001292303,0.00001110536,0.000004635676,0.00002104121,0.0001602431],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9822148,"threshold_uncertainty_score":0.9999127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01880233710480925,"score_gpt":0.2766415926385166,"score_spread":0.2578392555337073,"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."}}