{"id":"W1777638167","doi":"10.1007/978-3-540-75690-3_12","title":"Detecting, Localizing and Classifying Visual Traits from Arbitrary Viewpoints Using Probabilistic Local Feature Modeling","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face recognition and analysis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Viewpoints; Invariant (physics); Classifier (UML); Trait; Computer vision; Probabilistic logic; Scale-invariant feature transform; Bayesian probability; Feature extraction; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006029363,0.0006811854,0.001306758,0.001092671,0.0002457811,0.0009973794,0.001362519,0.000694461,0.0008218571],"category_scores_gemma":[0.001169399,0.0005334485,0.001252581,0.001170289,0.0006438516,0.00123808,0.0009516335,0.0007303666,0.0006951361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003737828,"about_ca_system_score_gemma":0.000315418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001667004,"about_ca_topic_score_gemma":0.002352265,"domain_scores_codex":[0.9994615,0.00006139185,0.00001520045,0.0002070517,0.0001842925,0.00007051678],"domain_scores_gemma":[0.99939,0.0002024702,0.0001115923,0.000157356,0.0001041784,0.00003439854],"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":[0.0001905863,0.0001107327,0.005164318,0.0001462193,0.0001030978,0.0001796862,0.0001992206,0.06789118,0.166254,0.00393859,0.001754388,0.754068],"study_design_scores_gemma":[0.000008442586,0.0001134145,0.005984303,0.00001229659,0.00006763922,0.0005162536,0.00007075137,0.9506314,0.03439106,0.007173723,0.0009963802,0.00003435781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02870816,0.0001411185,0.9699645,0.00003295697,0.00000809424,0.00002448752,0.00004345162,0.0005870507,0.0004901361],"genre_scores_gemma":[0.4461112,0.0004164279,0.5505815,0.00006310063,0.00003370275,0.00006957586,0.0003540838,0.0002529343,0.002117489],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001667004,"threshold_uncertainty_score":0.003314614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04753793899810804,"score_gpt":0.2865872323069171,"score_spread":0.239049293308809,"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."}}