{"id":"W4252175914","doi":"10.32920/ryerson.14654502","title":"Real Time Video Stitching Implementation on a ZYNQ FPGA SoC","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Ontario Tech University","funders":"","keywords":"Image stitching; Computer science; Field-programmable gate array; Frame rate; Schematic; Embedded system; Computer hardware; Video processing; Firmware; Pipeline (software); FPGA prototype; Frame (networking); Artificial intelligence; Operating system; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003495674,0.0002983123,0.0003252728,0.0001582929,0.0001223132,0.0005811581,0.0009706065,0.0001695451,0.0002617462],"category_scores_gemma":[0.00005033721,0.0002826298,0.0001712437,0.0002283803,0.00002073049,0.0006165955,0.001990553,0.0005137366,0.00008693947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00019159,"about_ca_system_score_gemma":0.0002422705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002998777,"about_ca_topic_score_gemma":0.00001884016,"domain_scores_codex":[0.9978523,0.000107387,0.0003930793,0.0008818676,0.0004413468,0.0003239948],"domain_scores_gemma":[0.9983651,0.0001195395,0.0002192749,0.001045484,0.000158941,0.00009168099],"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":[0.0000150333,0.0001152684,0.00004719762,0.00008300986,0.00009719351,0.0001381049,0.001101716,0.0001408661,0.01999363,0.01485735,0.009299452,0.9541112],"study_design_scores_gemma":[0.0006271478,0.0004550364,0.001418585,0.000550029,0.00005332341,0.00002549036,0.0002129152,0.01160839,0.9204845,0.05701951,0.005977099,0.001568004],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00599375,0.00005866715,0.9793324,0.0006310079,0.0002853029,0.0004092998,0.000007822073,0.001076536,0.01220516],"genre_scores_gemma":[0.1197412,0.0006733301,0.8753153,0.001350504,0.0003300008,0.000118241,0.0001881873,0.00005217021,0.00223103],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9525432,"threshold_uncertainty_score":0.9999626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02528337334092695,"score_gpt":0.3542445208862896,"score_spread":0.3289611475453627,"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."}}