{"id":"W2766434144","doi":"10.1109/tsp.2017.8076059","title":"Learning-Based multilabel random walks for image segmentation containing translucent overlapped objects","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Pattern recognition (psychology); Pixel; Image segmentation; Geodesic; Image (mathematics); Manifold (fluid mechanics); Nonlinear dimensionality reduction; Computer vision; 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":[0.0004099102,0.0001728118,0.0002283263,0.0000665129,0.0006769391,0.0004822204,0.0006695518,0.00006109022,0.00001423691],"category_scores_gemma":[0.0003600844,0.0001518001,0.0001292688,0.00005606301,0.00006363617,0.001323385,0.00007209184,0.000135628,0.000009082659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004961574,"about_ca_system_score_gemma":0.00006254971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003102506,"about_ca_topic_score_gemma":0.00001178481,"domain_scores_codex":[0.9987902,0.00004590391,0.0002376808,0.0003986181,0.0002085588,0.0003190156],"domain_scores_gemma":[0.9987527,0.0002363175,0.000221634,0.0005358249,0.0001700773,0.00008347517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001231962,0.0002434718,0.0006298812,0.0001402171,0.00005961723,0.00004553547,0.001764197,0.0003279402,0.2689873,0.003708582,0.0005939145,0.7222673],"study_design_scores_gemma":[0.00780474,0.0006544574,0.0005107252,0.00004843785,0.00001412418,0.00000244613,0.0000894672,0.1971228,0.7901775,0.001342877,0.001928182,0.0003041849],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002376057,0.00003912662,0.9939464,0.0004520807,0.0001312394,0.0008096339,0.000002803252,0.0004894806,0.001753186],"genre_scores_gemma":[0.6704202,0.00001697007,0.3287482,0.0002921761,0.00003103731,0.00008399221,0.00000752744,0.00001338049,0.0003864496],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7219632,"threshold_uncertainty_score":0.6190227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02269828327090703,"score_gpt":0.3281703108859808,"score_spread":0.3054720276150738,"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."}}