{"id":"W2292408058","doi":"10.2390/biecoll-icvs2007-59","title":"Maximum-Likelihood Stereo Correspondence using Field Programmable Gate Arrays","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Field-programmable gate array; Software; Gate array; Software implementation; Computer science; Frame rate; Frame (networking); Implementation; Field (mathematics); Stereopsis; Computer hardware; Artificial intelligence; Computer vision; Maximum likelihood; Algorithm; 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":[0.0001791949,0.0001383496,0.0001472259,0.00008760743,0.0000885375,0.0002474704,0.0007067755,0.00003118301,0.0003489534],"category_scores_gemma":[0.00002683673,0.0001172777,0.00005638658,0.0003648568,0.00001454593,0.001104563,0.0003187273,0.0001497434,0.0006818284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002846431,"about_ca_system_score_gemma":0.00006246427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002787973,"about_ca_topic_score_gemma":0.000003089199,"domain_scores_codex":[0.9986648,0.00003139614,0.0001904474,0.0004237949,0.0002581707,0.0004313742],"domain_scores_gemma":[0.9990209,0.00007624202,0.00007175501,0.000645243,0.00007090386,0.0001149613],"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":[0.00003888431,0.0001093696,0.004859178,0.00004200113,0.00001502581,0.00005337102,0.0005457018,0.0007352019,0.02764109,0.009674723,0.001172346,0.9551131],"study_design_scores_gemma":[0.0005270794,0.000179075,0.0001450434,0.00008059658,0.000003097639,0.0000639809,0.0001423743,0.9484308,0.01948498,0.01186069,0.01872195,0.0003602882],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02223722,0.00004980816,0.9655282,0.0004196146,0.0006546836,0.0002192097,2.337151e-7,0.0002522638,0.01063874],"genre_scores_gemma":[0.3128319,0.000007947127,0.6802461,0.002555103,0.00003746051,0.000004968369,5.76274e-7,0.00001403138,0.004301928],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9547528,"threshold_uncertainty_score":0.8763751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01573406502125314,"score_gpt":0.2794495888027823,"score_spread":0.2637155237815291,"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."}}