{"id":"W2031383908","doi":"10.1007/s00138-009-0202-2","title":"A topological approach to finding grids in calibration patterns","year":2009,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Quadrilateral; Delaunay triangulation; Grid; Constrained Delaunay triangulation; Triangulation; Computer science; Matching (statistics); Computer vision; Corner detection; Topology (electrical circuits); Artificial intelligence; Algorithm; Polygon mesh; Regular grid; Mathematics; Image (mathematics); Computer graphics (images); Geometry; Combinatorics; Finite element method","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":[0.00005643863,0.00007915121,0.00008466485,0.00009974233,0.00005555463,0.00003940547,0.00005099273,0.00004813821,0.00001085437],"category_scores_gemma":[0.000004734235,0.00006829367,0.00001439686,0.0002283052,0.000006184191,0.00004233883,0.000009972964,0.00007560542,0.000004848191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001757526,"about_ca_system_score_gemma":0.000002046499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001197149,"about_ca_topic_score_gemma":0.00000833707,"domain_scores_codex":[0.9995416,0.00001122259,0.0001423556,0.0001408973,0.00006191777,0.0001019518],"domain_scores_gemma":[0.9997982,0.000013846,0.000009373709,0.0001066721,0.000007356175,0.00006457989],"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":[0.000009143939,0.0002881842,0.007529308,0.00004274529,0.00000486874,0.000001270684,0.0003437508,0.7493649,0.007122022,0.1090877,0.0006029923,0.1256032],"study_design_scores_gemma":[0.0001650395,0.00003838165,0.02992257,0.000008935249,0.000002676738,0.000002210808,0.00002737462,0.9662222,0.0002223402,0.0009555541,0.002317445,0.000115275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09693728,0.00004488029,0.8997792,0.0005074908,0.00001483465,0.0002859255,0.00000899279,0.0001098771,0.002311516],"genre_scores_gemma":[0.9965707,0.00003443424,0.002916065,0.000289516,0.00003932191,0.00003939633,0.00007665963,0.000007008587,0.00002690123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8996334,"threshold_uncertainty_score":0.2784935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01106363724893539,"score_gpt":0.2557678939482693,"score_spread":0.2447042566993339,"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."}}