{"id":"W2161017703","doi":"10.1109/icma.2011.5985611","title":"Object detection by parts using appearance, structural and shape features","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; China Scholarship Council","keywords":"Artificial intelligence; Object detection; Computer vision; Computer science; Classifier (UML); Pattern recognition (psychology); Viola–Jones object detection framework; Support vector machine; Object (grammar); Segmentation; Detector; Orientation (vector space); A priori and a posteriori; Feature extraction; Mathematics; Face detection","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001086994,0.0008498305,0.001666417,0.00210192,0.0004027516,0.001194475,0.001525656,0.001063899,0.001237723],"category_scores_gemma":[0.002225553,0.000776566,0.00159866,0.001040457,0.0008190754,0.002101337,0.001071185,0.0006698435,0.000950711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000392019,"about_ca_system_score_gemma":0.0006213851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009763733,"about_ca_topic_score_gemma":0.001540163,"domain_scores_codex":[0.9992428,0.00008641507,0.00003853768,0.0002234929,0.000340019,0.00006871238],"domain_scores_gemma":[0.9984506,0.0005816647,0.0002170612,0.0002771065,0.0003786784,0.00009472388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002826196,0.0001343111,0.005663021,0.0001960927,0.0001737606,0.000236594,0.0001051528,0.01949072,0.2285129,0.002990977,0.001741613,0.7404722],"study_design_scores_gemma":[0.00004317176,0.0004210765,0.01732508,0.00006039076,0.0002185804,0.001749866,0.00007442287,0.7837493,0.1752418,0.01058565,0.01040197,0.0001288183],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02271095,0.0003115633,0.9747366,0.00005473175,0.00003769466,0.00005447394,0.00004494685,0.001401811,0.0006471149],"genre_scores_gemma":[0.1802208,0.0003384815,0.8167198,0.0001450693,0.00005733854,0.0000916254,0.0002621395,0.0002415952,0.001923245],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00210192,"threshold_uncertainty_score":0.00574863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02443536598010231,"score_gpt":0.2532955849916202,"score_spread":0.2288602190115179,"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."}}