{"id":"W2167075585","doi":"10.1109/fuzzy.2007.4295670","title":"Image-to-X Registration using Linear Features","year":2007,"lang":"en","type":"article","venue":"Proceedings of ... IEEE International Conference on Fuzzy Systems","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Geospatial-Intelligence Agency; National Science Foundation","keywords":"Computer science; Matching (statistics); Artificial intelligence; Process (computing); Computer vision; Pixel; Image registration; Pattern recognition (psychology); Aerial image; Template matching; Image resolution; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002987733,0.0001630694,0.0001717548,0.0003177624,0.000045105,0.0001213733,0.0002343072,0.000118573,0.00001080489],"category_scores_gemma":[0.00005398502,0.0001610285,0.00004815228,0.0001756977,0.0000245974,0.0003403796,0.00001181738,0.0001836203,0.00002834549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001583967,"about_ca_system_score_gemma":0.00002171641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007798283,"about_ca_topic_score_gemma":0.000004179178,"domain_scores_codex":[0.9987696,0.00000222298,0.0003920039,0.0001977003,0.0004432178,0.0001952783],"domain_scores_gemma":[0.9990557,0.00002244869,0.0001663232,0.0000593476,0.0006155935,0.00008052935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006220448,0.00003228908,0.0003437602,0.0001425346,0.00005738078,0.000002974308,0.0001690372,0.004356839,0.9691584,0.02117458,0.0037757,0.0007243535],"study_design_scores_gemma":[0.0008637662,0.0003229606,0.008089473,0.00239636,0.00005605313,0.0002250997,0.001876838,0.5132176,0.4663664,0.0008211148,0.004776604,0.0009877328],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8400928,0.00003020171,0.006191365,0.0001331919,0.002984956,0.0002807956,0.00001928182,0.000397587,0.1498698],"genre_scores_gemma":[0.9963366,0.00001656506,0.002714002,0.00001769285,0.000558909,0.000007662469,0.000006796138,0.00002588252,0.0003158819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5088608,"threshold_uncertainty_score":0.6566554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04164739441197009,"score_gpt":0.308842422411205,"score_spread":0.2671950279992349,"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."}}