{"id":"W2160395623","doi":"10.1109/sibgrapi.2005.38","title":"Performance Analysis of Oriented Feature Detectors","year":2005,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detector; Gabor filter; Artificial intelligence; Gaussian filter; Computer science; Computer vision; Filter (signal processing); Gaussian; Feature (linguistics); Feature extraction; Pattern recognition (psychology); Matched filter; Image (mathematics); Physics; Telecommunications","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":[0.00009522653,0.00005138318,0.0001051122,0.0002076657,0.00003060536,0.00001523704,0.000325975,0.00003269191,0.00005328647],"category_scores_gemma":[0.0000104981,0.00003827089,0.00007199895,0.001842505,0.00002114329,0.0002562302,0.00004881417,0.00004549206,0.00001150383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000181709,"about_ca_system_score_gemma":0.00001486924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002559716,"about_ca_topic_score_gemma":0.000003768504,"domain_scores_codex":[0.9994995,0.00001136322,0.0001113041,0.0001365553,0.0001543466,0.00008690557],"domain_scores_gemma":[0.9994757,0.00001539342,0.00005751964,0.0003217066,0.0001011383,0.00002858189],"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":[0.00001428192,0.000188595,0.06274007,0.00002378129,0.0004402119,8.591404e-7,0.0006113681,0.0001092999,0.04239596,0.03603973,0.001765248,0.8556706],"study_design_scores_gemma":[0.00005344647,0.00003578755,0.07929299,0.000002648881,0.0000482779,6.891666e-7,0.000006978613,0.3322177,0.5766233,0.00001046632,0.01162422,0.00008349495],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1117421,0.00003256085,0.8815418,0.0006373244,0.00002378764,0.00004637066,8.744952e-7,0.0002284553,0.005746683],"genre_scores_gemma":[0.9298927,0.00002060193,0.06809413,0.00009197876,0.000009343348,0.000002489623,0.000001451823,0.000001488993,0.001885814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8555871,"threshold_uncertainty_score":0.1560642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008748287779171417,"score_gpt":0.2384193610527765,"score_spread":0.2296710732736051,"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."}}