{"id":"W2101043348","doi":"","title":"Improving Depth Maps by Nonlinear Diffusion","year":2004,"lang":"en","type":"article","venue":"Digital Library (University of West Bohemia)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Depth map; Segmentation; 2D to 3D conversion; Computer graphics (images); Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004616246,0.001063844,0.0005644818,0.0008491125,0.0003694542,0.0006023124,0.0008291455,0.0009104754,0.003026877],"category_scores_gemma":[0.002145751,0.000421947,0.0004727645,0.0007129455,0.0005389159,0.001497636,0.001629428,0.00103433,0.0007464951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008872307,"about_ca_system_score_gemma":0.0006826756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006237971,"about_ca_topic_score_gemma":0.006517771,"domain_scores_codex":[0.9997689,0.00003753139,0.000009550378,0.00004922093,0.0001045387,0.00003024433],"domain_scores_gemma":[0.9996132,0.0001510298,0.00004546441,0.00006069028,0.0001025413,0.00002698164],"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":[0.0003279385,0.0001338235,0.0005722449,0.0002325379,0.00007132193,0.0001075697,0.0002268304,0.3625174,0.1536311,0.02826082,0.005681493,0.448237],"study_design_scores_gemma":[0.00002906564,0.0000369324,0.0001603535,0.000006496669,0.00001105569,0.0000466314,0.00001051457,0.9784411,0.01312848,0.005848843,0.002264364,0.00001613431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0262637,0.0005789147,0.9691721,0.0002303102,0.00007004596,0.00003850052,0.00006943277,0.001106699,0.00247032],"genre_scores_gemma":[0.3360094,0.001145711,0.6530306,0.0001557563,0.0001188536,0.00008886187,0.0002025298,0.0003056266,0.008942527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006237971,"threshold_uncertainty_score":0.01240337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005032317221786666,"score_gpt":0.1718537349980483,"score_spread":0.1668214177762616,"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."}}