{"id":"W2050735977","doi":"10.4108/icst.immerscom2007.2114","title":"Fast Stroke Matching by Angle Quantization","year":2007,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Quantization (signal processing); Computer science; Similarity (geometry); Matching (statistics); Feature (linguistics); Feature matching; Dimension (graph theory); Artificial intelligence; Computer vision; Point (geometry); Space (punctuation); Task (project management); Pattern recognition (psychology); Feature extraction; Mathematics; Geometry; Image (mathematics); Combinatorics; Engineering","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":[0.0005409706,0.0005750051,0.001183448,0.001653301,0.0003736656,0.001163856,0.00108086,0.0006480434,0.006562123],"category_scores_gemma":[0.002600862,0.0003371554,0.0004854681,0.002500863,0.0004303189,0.002076354,0.001338983,0.0007747562,0.002402763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003743309,"about_ca_system_score_gemma":0.0007513039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002602412,"about_ca_topic_score_gemma":0.002600562,"domain_scores_codex":[0.998853,0.000138376,0.000114672,0.0002756951,0.0005195362,0.00009878082],"domain_scores_gemma":[0.9992505,0.0001928973,0.00006629024,0.0002392313,0.0002123958,0.00003875026],"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":[0.0002611339,0.00005903133,0.0004980129,0.00007831187,0.0000326351,0.00004797255,0.00005382168,0.01545452,0.02895659,0.01017959,0.005121343,0.939257],"study_design_scores_gemma":[0.0001305673,0.0002647067,0.002091199,0.0000313723,0.00002894871,0.0004382702,0.00008280214,0.8968447,0.0558096,0.02642359,0.01779328,0.00006096777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009871023,0.00040643,0.9864117,0.00005097649,0.0001135749,0.00005972342,0.0001226007,0.001666429,0.001297518],"genre_scores_gemma":[0.2497065,0.0005067315,0.7433042,0.0001066653,0.00009538134,0.000160004,0.0008060852,0.0002381012,0.005076334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006562123,"threshold_uncertainty_score":0.02195245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218096906038022,"score_gpt":0.248762383570032,"score_spread":0.2365814145096518,"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."}}