{"id":"W2414795363","doi":"10.5194/isprs-archives-xli-b3-769-2016","title":"A FAST APPROACH FOR STITCHING OF AERIAL IMAGES","year":2016,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Cheng Kung University; Canada Research Chairs","keywords":"Image stitching; Artificial intelligence; Scale-invariant feature transform; Computer vision; Computer science; Panorama; Aerial image; Feature (linguistics); Image registration; Computation; Focus (optics); Image (mathematics); Algorithm","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":[0.001051828,0.0003609266,0.0004159083,0.0007464777,0.0007385905,0.0004067072,0.001188824,0.00008811912,0.000004441914],"category_scores_gemma":[0.0006218037,0.000208712,0.0003518128,0.0006159693,0.002148736,0.0004413282,0.000427247,0.0002268057,0.000001002917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004224928,"about_ca_system_score_gemma":0.0001387622,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3761725,"about_ca_topic_score_gemma":0.03387293,"domain_scores_codex":[0.9964866,0.0001586082,0.001340708,0.0002998857,0.001277386,0.0004367982],"domain_scores_gemma":[0.9971563,0.0009724853,0.001065718,0.0003802736,0.0003072158,0.0001180577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001334768,0.00001497675,0.0001001635,0.00006883641,0.00008782806,7.778065e-8,0.001622032,0.03920076,0.01159737,0.00001903363,0.0000500493,0.9471054],"study_design_scores_gemma":[0.0009054688,0.0001129869,0.0008491086,0.0003214627,0.00004141865,0.00004407996,0.0009725855,0.9668804,0.02486962,0.003521699,0.001222264,0.000258882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006873797,0.00002400035,0.9831695,0.001322953,0.001555394,0.0006637041,0.0001986915,0.00005261164,0.006139331],"genre_scores_gemma":[0.9886573,0.0001511217,0.01065007,0.0002721361,0.0001612023,5.219696e-7,0.00003880439,0.00001505753,0.00005378831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9817835,"threshold_uncertainty_score":0.9837564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01482857084689455,"score_gpt":0.2369344742225981,"score_spread":0.2221059033757036,"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."}}