{"id":"W2158008709","doi":"10.1109/icpr.2006.224","title":"Adaptive Step Size Window Matching for Detection","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Matching (statistics); Object detection; Window (computing); Set (abstract data type); Algorithm; Computational complexity theory; Point (geometry); Selection (genetic algorithm); Artificial intelligence; Pattern recognition (psychology); 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":[0.001519778,0.0004935481,0.0006845059,0.00140257,0.0004825647,0.0009057571,0.001388128,0.00087012,0.002596918],"category_scores_gemma":[0.007779134,0.0004143604,0.0004260888,0.001361722,0.0005170178,0.001987378,0.0009409461,0.0009167333,0.001226734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005080805,"about_ca_system_score_gemma":0.0008041729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009297981,"about_ca_topic_score_gemma":0.001186248,"domain_scores_codex":[0.9985587,0.00032204,0.00007244624,0.0002960271,0.0006443822,0.0001063692],"domain_scores_gemma":[0.9976636,0.001206871,0.0001382265,0.0004897739,0.0004171973,0.00008438442],"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":[0.000544116,0.0001421472,0.001507449,0.0001722473,0.00007997853,0.0001699693,0.0001062247,0.05202283,0.1403623,0.02887511,0.004838289,0.7711793],"study_design_scores_gemma":[0.00004585309,0.0002034122,0.001527918,0.00002456278,0.00004089541,0.0005321064,0.00003041629,0.8720705,0.1003358,0.01715212,0.007993876,0.00004258384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007689659,0.0002685698,0.9904389,0.00005298393,0.00003409793,0.00003636143,0.00001809878,0.0007546414,0.0007066034],"genre_scores_gemma":[0.2065463,0.0003700018,0.7910069,0.0001016822,0.00006168044,0.00008903252,0.0001076441,0.0001824543,0.001534371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002596918,"threshold_uncertainty_score":0.008687556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01766974875868356,"score_gpt":0.2653214840217811,"score_spread":0.2476517352630976,"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."}}