{"id":"W2050819065","doi":"10.1109/icspcs.2014.7021058","title":"2×2 Panoramic camera array stitching using edge matching","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Image stitching; Panorama; Scale-invariant feature transform; Computer vision; Artificial intelligence; Computer science; Matching (statistics); Enhanced Data Rates for GSM Evolution; Feature matching; Feature (linguistics); Invariant (physics); Feature extraction; 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":[0.000419079,0.0002021986,0.0002260945,0.0001317622,0.0002510739,0.0003085514,0.0008011649,0.00006646286,0.00002175761],"category_scores_gemma":[0.0001006356,0.0001761882,0.00008184281,0.0003672212,0.00003916227,0.001318441,0.0002544496,0.0002464243,0.00008129944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006730467,"about_ca_system_score_gemma":0.00003999368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001604905,"about_ca_topic_score_gemma":0.00001239533,"domain_scores_codex":[0.9985271,0.00008615897,0.0002709934,0.00045702,0.00025478,0.0004039171],"domain_scores_gemma":[0.9988566,0.0001349975,0.0001094172,0.0007129111,0.00007591418,0.0001101713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000668562,0.00007004268,0.0006112773,0.00003792545,0.0000218802,0.000007405055,0.001086996,0.0004818766,0.5435014,0.08903901,0.0004011329,0.3647344],"study_design_scores_gemma":[0.0007979874,0.0002279943,0.00080788,0.0003286377,0.00003170177,0.0001847039,0.00012993,0.06359396,0.5289454,0.267951,0.1349115,0.002089268],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03231987,0.00006722598,0.961124,0.0001690378,0.000242032,0.0001215124,5.398807e-7,0.0008688326,0.005086955],"genre_scores_gemma":[0.4204807,0.00001079963,0.5783705,0.0006896685,0.000104065,0.000002252546,0.00000103932,0.00001465654,0.0003264166],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3881608,"threshold_uncertainty_score":0.7184748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03063795919640527,"score_gpt":0.3083349745415288,"score_spread":0.2776970153451235,"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."}}