{"id":"W2020183999","doi":"10.1145/2168996.2168997","title":"Image registration for foveated panoramic sensing","year":2012,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Image registration; Field of view; Catadioptric system; Matching (statistics); Template matching; Parametric statistics; 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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003357311,0.0001761562,0.0001595987,0.0001631613,0.001319362,0.0001654666,0.001009839,0.00006399965,0.000002783078],"category_scores_gemma":[0.00005887373,0.0001867388,0.00007765282,0.0004775964,0.0001627842,0.0005969732,0.00008652834,0.0002530602,0.00002690439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005212228,"about_ca_system_score_gemma":0.00003520828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001778809,"about_ca_topic_score_gemma":0.000005404027,"domain_scores_codex":[0.9987942,0.00007819053,0.0003540592,0.0003165832,0.0001268829,0.0003300907],"domain_scores_gemma":[0.9961241,0.001094273,0.0001619896,0.002273691,0.0001835424,0.0001624294],"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":[0.000002616837,0.0001981023,0.00002492726,0.00001150179,0.00001618227,3.585553e-8,0.0005026925,0.00005960372,0.004544704,0.004935474,0.00005004551,0.9896541],"study_design_scores_gemma":[0.000525128,0.00003562532,0.000426031,0.00003881871,0.00003015383,0.0000185308,0.0002286209,0.952915,0.001457472,0.002888463,0.04114437,0.0002918522],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003126912,0.0002581727,0.994355,0.003507414,0.00009382691,0.0007438216,0.00001667218,0.0003646849,0.000347728],"genre_scores_gemma":[0.3701866,0.0001400076,0.6292523,0.0002151249,0.00004487762,0.00007850004,0.0000305883,0.00001379775,0.00003826155],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9893622,"threshold_uncertainty_score":0.9999808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03450006992051893,"score_gpt":0.3325540803361051,"score_spread":0.2980540104155861,"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."}}