{"id":"W2126731793","doi":"10.1109/igarss.1989.576073","title":"Image Matching Using Spatial Frequency Signatures","year":2005,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Spatial frequency; Matching (statistics); Artificial intelligence; Pattern recognition (psychology); Image resolution; Computer vision; Mathematics; Statistics; Optics; Physics","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":[0.0005948314,0.0003797471,0.0009265826,0.003784116,0.0004930715,0.001299643,0.0008645719,0.0009221336,0.003826486],"category_scores_gemma":[0.002250104,0.0003340607,0.0007175654,0.003519331,0.0004428363,0.002383585,0.0009900649,0.0004277939,0.002216459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004098123,"about_ca_system_score_gemma":0.0005966503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001177139,"about_ca_topic_score_gemma":0.001492256,"domain_scores_codex":[0.9991922,0.00009542926,0.00004514556,0.0001246376,0.000454504,0.00008813231],"domain_scores_gemma":[0.9989275,0.0001995753,0.0001226777,0.0003562799,0.0003457411,0.00004819876],"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":[0.0004362392,0.0001166089,0.001332991,0.0001346749,0.00007825307,0.00009190748,0.00005871917,0.005648672,0.1706251,0.007162447,0.002068558,0.8122458],"study_design_scores_gemma":[0.00009428309,0.000362757,0.006790532,0.00003989168,0.0002143531,0.001993258,0.0002113679,0.6239268,0.3296393,0.02104612,0.01560795,0.00007348939],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04790954,0.0005295492,0.9470882,0.0001249225,0.00009644406,0.00006309399,0.00009339995,0.001035083,0.003059726],"genre_scores_gemma":[0.3587303,0.0009252281,0.6317863,0.000174488,0.0001433587,0.00007612342,0.0004460725,0.0002490493,0.007468976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003826486,"threshold_uncertainty_score":0.01280093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01713283928943322,"score_gpt":0.2749669813618854,"score_spread":0.2578341420724522,"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."}}