{"id":"W1927906724","doi":"10.1007/978-3-642-02611-9_72","title":"The Distinction between Virtual and Physical Planes Using Homography","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Homography; Computer science; Convex hull; Plane (geometry); Computer vision; Artificial intelligence; Point (geometry); Regular polygon; Identification (biology); Computer graphics (images); Algorithm; Geometry; Mathematics","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":[0.0003596748,0.000338075,0.0003136405,0.0003298191,0.0007043276,0.000689704,0.00139266,0.00009794523,6.787848e-7],"category_scores_gemma":[0.00004122902,0.0002398416,0.00008119613,0.0004187943,0.000848961,0.0005687678,0.000679548,0.0005770803,0.000003824245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007311108,"about_ca_system_score_gemma":0.0001025341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005534851,"about_ca_topic_score_gemma":0.000006248413,"domain_scores_codex":[0.9977488,0.00002726185,0.0002622578,0.0009250304,0.0006007007,0.0004359691],"domain_scores_gemma":[0.9983253,0.0006079368,0.0001769649,0.0006792385,0.00008671104,0.0001238216],"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":[0.000001787997,0.00000555582,0.00009085009,0.000002907501,0.000003344596,0.000009269952,0.0002114309,0.001790175,0.00005387849,0.005538991,0.000002336655,0.9922895],"study_design_scores_gemma":[0.0001610194,0.0001831587,0.001113,0.0001889931,0.000009382455,0.00005548518,4.344354e-7,0.7888681,0.0002585337,0.2061541,0.002539592,0.0004682091],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006313667,0.0003163434,0.9973217,0.0004551598,0.0005687115,0.0001550477,0.000002390767,0.00009679989,0.0004524578],"genre_scores_gemma":[0.5941098,0.0001193967,0.4027539,0.00103823,0.00174315,0.000003102111,0.000005219289,0.00004007465,0.000187171],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9918213,"threshold_uncertainty_score":0.9780458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01605855896331048,"score_gpt":0.2678665338713688,"score_spread":0.2518079749080583,"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."}}