{"id":"W2113894747","doi":"10.1109/tpami.2006.207","title":"Graphical Models and Point Pattern Matching","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":131,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Australian Government; National ICT Australia","keywords":"Euclidean geometry; 3-dimensional matching; Matching (statistics); Algorithm; Time complexity; Graphical model; Mathematics; Graph; Euclidean distance; Point (geometry); Mathematical optimization; Polynomial; Dimension (graph theory); Blossom algorithm; Computer science; Artificial intelligence; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002963914,0.00024447,0.0003065208,0.0005580909,0.0002837016,0.0002242419,0.0003415053,0.00007020831,0.00004163611],"category_scores_gemma":[6.670886e-7,0.0002082601,0.000233657,0.000826817,0.000100917,0.0004080629,0.000009696412,0.000297274,0.000009589849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001063866,"about_ca_system_score_gemma":0.00000645856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001921649,"about_ca_topic_score_gemma":0.001079966,"domain_scores_codex":[0.998429,0.0001047916,0.0003600688,0.0005955256,0.0002418783,0.0002687498],"domain_scores_gemma":[0.9992176,0.0001227189,0.00007830992,0.0004136779,0.00004127983,0.0001263865],"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":[0.000006646779,0.0002125579,0.0006395463,0.00001748611,0.0002871162,0.0000286072,0.0004811337,0.05665629,0.0002465413,0.004590978,0.000003353838,0.9368297],"study_design_scores_gemma":[0.0001343142,0.0001055018,0.001600861,0.00002745781,0.0003019737,0.00004688354,0.00005536658,0.8776383,0.01769035,0.1019687,0.00001114002,0.0004191611],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02142722,0.0001355107,0.9775597,0.0004779226,0.0001096876,0.00008140492,0.0000261036,0.00009912279,0.00008330453],"genre_scores_gemma":[0.9972851,0.0001826665,0.001972145,0.0004373973,0.000019146,0.0000134008,0.000003343224,0.00001035461,0.00007646642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9758579,"threshold_uncertainty_score":0.8492599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01418689186455034,"score_gpt":0.241652290326984,"score_spread":0.2274653984624336,"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."}}