{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001379668,0.00008649776,0.00007603822,0.0000652513,0.0001039057,0.0001806668,0.0004962109,0.00004883511,0.0001194173],"category_scores_gemma":[0.00001392307,0.00007101107,0.00004429192,0.0001759092,0.00002665697,0.0007839653,0.00009980702,0.0001113375,0.00007231584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004576268,"about_ca_system_score_gemma":0.00004068698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001627098,"about_ca_topic_score_gemma":0.000007606498,"domain_scores_codex":[0.9992583,0.00002701497,0.0001517497,0.0002131697,0.0001838691,0.0001658388],"domain_scores_gemma":[0.9995182,0.00002225836,0.00005187217,0.0002937543,0.00006719308,0.00004675602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001014516,0.00003870838,0.00002470247,0.000005799113,0.000004337165,0.000004999878,0.0001852541,0.000005482326,0.789923,0.0879877,0.0002913136,0.1215277],"study_design_scores_gemma":[0.0001606386,0.00003393211,0.0009785478,0.00001822295,0.000004934273,0.00002321231,0.00002202784,0.2365978,0.724802,0.03418644,0.002810805,0.0003615518],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002440346,0.00007650136,0.9844043,0.00116802,0.00006799942,0.00007198817,5.788988e-7,0.0005194491,0.01125081],"genre_scores_gemma":[0.4540809,0.000004295149,0.5453062,0.0003546344,0.00008652565,0.000001679721,4.817792e-7,0.000003899002,0.0001613247],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4516406,"threshold_uncertainty_score":0.2895747,"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."}}