{"id":"W2102503069","doi":"10.1109/igarss.2002.1026106","title":"Automatic registration of SAR and visible band remote sensing images","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Image registration; Synthetic aperture radar; Matching (statistics); Remote sensing; Radar imaging; Image processing; Image (mathematics); Radar; Geography; Mathematics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003654339,0.00005060396,0.0000820786,0.0000528067,0.00003034874,0.00005966574,0.00008292312,0.00002327906,0.0000375297],"category_scores_gemma":[0.0001876423,0.00004351942,0.00001239763,0.0001022789,0.00005301992,0.0002966477,0.00001823305,0.00003473262,0.000002634471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008584451,"about_ca_system_score_gemma":0.00002739186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004155908,"about_ca_topic_score_gemma":0.000001439366,"domain_scores_codex":[0.9993612,0.00006306388,0.0001875337,0.0001384723,0.0001719186,0.0000778279],"domain_scores_gemma":[0.9995586,0.00006695805,0.0000784302,0.000211726,0.00004888866,0.00003538527],"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":[5.139931e-7,0.00001203902,0.00002424501,0.00006499883,0.000005919429,0.000007751781,0.0002201275,7.106852e-7,0.1418562,0.007586864,0.002672035,0.8475486],"study_design_scores_gemma":[0.0001205934,0.00003703697,0.0002002229,0.00003319877,0.00000266634,0.00002745236,0.00002467292,0.04420308,0.9469402,0.008265665,0.00008447891,0.00006077499],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004582272,0.00003940474,0.9799448,0.0002225211,0.00002806379,0.00008385469,1.284439e-7,0.0001501034,0.01494881],"genre_scores_gemma":[0.1889599,0.0000127095,0.8105007,0.0001871435,0.000002533974,5.543732e-8,3.094297e-7,0.000002234099,0.0003344211],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8474878,"threshold_uncertainty_score":0.177467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01483512110386327,"score_gpt":0.2811777671166585,"score_spread":0.2663426460127953,"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."}}