{"id":"W2806272023","doi":"10.1109/plans.2018.8373478","title":"Evaluation of feature points descriptors' performance for visual finger printing localization of smartphones","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Scale-invariant feature transform; Artificial intelligence; Computer science; Histogram; Computer vision; RGB color model; Feature (linguistics); Histogram of oriented gradients; Pattern recognition (psychology); Matching (statistics); Feature extraction; Image (mathematics); 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":[0.0009855683,0.0008234127,0.0009575106,0.001920592,0.0002472701,0.0007889197,0.0005778486,0.0005617033,0.001566645],"category_scores_gemma":[0.004709975,0.0001362373,0.0005806083,0.001200222,0.0002422398,0.0008132569,0.0005493276,0.0002466918,0.0006858253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004301578,"about_ca_system_score_gemma":0.0004180302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005681418,"about_ca_topic_score_gemma":0.003081789,"domain_scores_codex":[0.9990026,0.0001298806,0.0001114395,0.0001867689,0.0004113756,0.0001579754],"domain_scores_gemma":[0.9983884,0.0005596355,0.0001560659,0.0001807441,0.00063424,0.00008099456],"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":[0.004840918,0.0005996276,0.02517927,0.001270714,0.0003771647,0.0006418363,0.0002701877,0.05367452,0.08515,0.00088742,0.00469464,0.8224137],"study_design_scores_gemma":[0.0002402327,0.006749068,0.07874509,0.0001021379,0.0003493522,0.001540017,0.0008465204,0.7532632,0.1529533,0.0004587522,0.004608909,0.0001433707],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9344279,0.002517125,0.05612807,0.0001309632,0.000213151,0.0001982677,0.0009171088,0.002782118,0.00268526],"genre_scores_gemma":[0.9661531,0.0005579384,0.03057123,0.00002365588,0.00002339743,0.00006662575,0.00122283,0.00004726586,0.00133394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005681418,"threshold_uncertainty_score":0.01129669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0371742511569691,"score_gpt":0.3348740266784874,"score_spread":0.2976997755215183,"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."}}