{"id":"W2017204914","doi":"10.1109/ism.2012.46","title":"Mutual Information Based Stereo Correspondence in Extreme Cases","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Mutual information; Markov random field; Computer science; Artificial intelligence; Ambiguity; Context (archaeology); Markov chain; Matching (statistics); Term (time); Markov process; Pattern recognition (psychology); Computer vision; Mathematics; Image (mathematics); Machine learning; Image segmentation; Statistics; Geography","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":[0.002542541,0.000829765,0.001427407,0.002490679,0.000813088,0.0013879,0.001778633,0.001919569,0.002243703],"category_scores_gemma":[0.00854177,0.0007658346,0.0008463822,0.001640256,0.001698128,0.003187475,0.003907888,0.001445274,0.0005101298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007973664,"about_ca_system_score_gemma":0.0007430158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001145946,"about_ca_topic_score_gemma":0.001275652,"domain_scores_codex":[0.9967586,0.0007966637,0.0001305265,0.0004623109,0.001630909,0.0002209634],"domain_scores_gemma":[0.9965627,0.00170075,0.0005487339,0.000616638,0.0004541184,0.0001170251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005045708,0.0001325044,0.003177536,0.0002888333,0.0001685257,0.001202761,0.0005227248,0.5645972,0.02536721,0.123174,0.002917143,0.2779469],"study_design_scores_gemma":[0.00001256443,0.00003775349,0.0007267505,0.00001568235,0.0000163432,0.0005426275,0.00005730909,0.9510873,0.007136906,0.03877094,0.001560533,0.00003533837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02056599,0.0001725053,0.9766108,0.00008999181,0.00001471744,0.00002497031,0.00003565124,0.0002799511,0.002205501],"genre_scores_gemma":[0.5898505,0.0003346124,0.4075165,0.0001006545,0.00006629071,0.00009827863,0.0001823062,0.0001584997,0.001692315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002542541,"threshold_uncertainty_score":0.01344639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07993111315244605,"score_gpt":0.2890484060172989,"score_spread":0.2091172928648529,"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."}}