{"id":"W2184426797","doi":"10.20385/1860-2037/9.2012.4","title":"Sharpness Matching in Stereo Images","year":2010,"lang":"en","type":"article","venue":"Hochschule Düsseldorf","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer vision; Discrete cosine transform; Brightness; Computer stereo vision; Matching (statistics); Computer science; Image (mathematics); Mathematics; Stereo image; Stereopsis; Pattern recognition (psychology); Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008917179,0.0004760589,0.0006943409,0.001793926,0.0003542244,0.001114932,0.0009344123,0.001033962,0.002528695],"category_scores_gemma":[0.004693636,0.0006313322,0.0007582335,0.001079293,0.0006017617,0.001448515,0.001452223,0.0009104498,0.0008958618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005455355,"about_ca_system_score_gemma":0.000465532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001325279,"about_ca_topic_score_gemma":0.001201205,"domain_scores_codex":[0.998669,0.0001302763,0.00006074118,0.0002374574,0.0007860519,0.0001163959],"domain_scores_gemma":[0.9987801,0.0002554368,0.0001942653,0.0003214077,0.0003987906,0.00005000665],"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":[0.0005506657,0.00008752856,0.001628895,0.0003251077,0.0001135467,0.0005018734,0.0003709046,0.03628187,0.4788584,0.01211797,0.002065443,0.4670978],"study_design_scores_gemma":[0.0001245488,0.0006022094,0.01305195,0.00007796412,0.0001324446,0.002846803,0.0003227285,0.3558412,0.5906577,0.02097007,0.01521074,0.0001616474],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05904742,0.000314944,0.9372158,0.00007336617,0.00006968143,0.00008643336,0.00009479251,0.0008056986,0.002291939],"genre_scores_gemma":[0.3337175,0.0005022584,0.6622074,0.00017279,0.00006412275,0.00007367333,0.0003170857,0.0001962383,0.002748996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002528695,"threshold_uncertainty_score":0.00845933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006839829390956273,"score_gpt":0.26844547859941,"score_spread":0.2616056492084537,"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."}}