{"id":"W2614991575","doi":"10.1016/j.compeleceng.2017.04.032","title":"Feature points for multisensor images","year":2017,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Artificial intelligence; Feature (linguistics); Affine transformation; Pattern recognition (psychology); Computer vision; Focus (optics); Corner detection; Computer science; Scale-invariant feature transform; Detector; Feature extraction; Feature detection (computer vision); Mathematics; Image (mathematics); Image processing","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":[0.0001244171,0.000201912,0.0002471072,0.0001128637,0.0002807934,0.0004245042,0.001389516,0.00008704372,5.01726e-7],"category_scores_gemma":[0.0004269766,0.000193937,0.0001308461,0.0001420532,0.00002402515,0.0007305864,0.0002823758,0.000234433,0.000006440782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006189115,"about_ca_system_score_gemma":0.0000195457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001750871,"about_ca_topic_score_gemma":7.15936e-8,"domain_scores_codex":[0.9988282,0.000008645325,0.0001296289,0.0004152194,0.0001539134,0.0004644055],"domain_scores_gemma":[0.9987051,0.0002029334,0.00008992381,0.0007752158,0.000093927,0.0001328341],"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":[0.00002398513,0.0000716068,0.0001035695,0.00007051314,0.00005736273,0.00009233921,0.00005845053,0.0007912686,0.03119899,0.02535499,0.02484751,0.9173294],"study_design_scores_gemma":[0.0006045795,0.0001971874,0.002428171,0.00005545581,0.000008150036,0.00003933883,2.958479e-7,0.8042898,0.144697,0.001482954,0.0457537,0.0004433424],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002624796,0.0003195355,0.9963511,0.001676967,0.000369687,0.0002731365,0.000002324591,0.0006738082,0.00007095506],"genre_scores_gemma":[0.1089878,0.00004949775,0.8902313,0.0002226991,0.0002377452,0.00003041121,0.000002061897,0.00002680553,0.0002116722],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9168861,"threshold_uncertainty_score":0.7908521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01215059679017128,"score_gpt":0.2732447909876362,"score_spread":0.2610941941974649,"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."}}