{"id":"W2096781507","doi":"10.1109/icip.1998.999019","title":"Selective image diffusion: application to disparity estimation","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Regularization (linguistics); Computer science; Inverse problem; Term (time); A priori and a posteriori; Bayesian probability; Eigenvalues and eigenvectors; Algorithm; Computation; Transformation (genetics); Context (archaeology); Diffusion; Image processing; Image (mathematics); Artificial intelligence; Anisotropic diffusion; Mathematical optimization; Applied mathematics; Mathematics; Mathematical analysis; Physics","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":[0.0002363687,0.0003911028,0.0003238182,0.000680722,0.0002556988,0.0003414592,0.0003186156,0.0007408589,0.0011686],"category_scores_gemma":[0.0009535726,0.000170592,0.0002873112,0.0007199692,0.0003847477,0.0003783108,0.0007709941,0.0003666643,0.0001985985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003320821,"about_ca_system_score_gemma":0.0002817535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002411885,"about_ca_topic_score_gemma":0.001525858,"domain_scores_codex":[0.9999217,0.00001718231,0.000004139554,0.00001665627,0.00003230057,0.000007968614],"domain_scores_gemma":[0.9998648,0.00007211562,0.00001521697,0.00001215967,0.00002665165,0.000008966836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001480236,0.00006706137,0.001313566,0.000352594,0.00005775766,0.0004941095,0.0003017067,0.2249447,0.2483968,0.07522615,0.002182814,0.4465147],"study_design_scores_gemma":[0.00002382522,0.00004178084,0.0006539482,0.00001431163,0.00001300536,0.0004330945,0.00002488726,0.9530444,0.02341578,0.01752966,0.004790407,0.00001497619],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03931209,0.00183626,0.9539448,0.000609464,0.00005896653,0.00004913006,0.00004437659,0.000451055,0.003693894],"genre_scores_gemma":[0.5187372,0.003514064,0.4727849,0.0001248451,0.0001153009,0.00006585151,0.00007349819,0.000100664,0.004483729],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002411885,"threshold_uncertainty_score":0.00479573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093091622261577,"score_gpt":0.2788137288571699,"score_spread":0.2678828126345541,"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."}}