{"id":"W1950112384","doi":"10.1109/cvpr.2015.7298913","title":"Real-time coarse-to-fine topologically preserving segmentation","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":135,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Segmentation; Markov random field; Computer science; Task (project management); Artificial intelligence; Image segmentation; Boundary (topology); Markov chain; Pattern recognition (psychology); Algorithm; Machine learning; 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":[0.0002829279,0.00008994959,0.0001079671,0.00006013018,0.00004301441,0.0000934167,0.0005919062,0.00003671159,0.00007760947],"category_scores_gemma":[0.0001761553,0.00007072121,0.00002435499,0.0003246633,0.00001683342,0.0006946042,0.0004285771,0.0000515239,0.0002532974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004950344,"about_ca_system_score_gemma":0.00003414772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005989422,"about_ca_topic_score_gemma":0.000003959838,"domain_scores_codex":[0.9991216,0.00004248673,0.0001589022,0.0002620338,0.0002228469,0.0001921257],"domain_scores_gemma":[0.9992026,0.00005277175,0.00004134076,0.0003903047,0.0001475147,0.0001654809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009227092,0.0002287788,0.0008742306,0.00001777052,0.00002951664,0.00009329818,0.001454814,0.0002452772,0.4030556,0.08624084,0.1457141,0.3619535],"study_design_scores_gemma":[0.0007862531,0.001524893,0.001030407,0.00003458838,0.000008925713,0.0000316594,0.0001026309,0.02829075,0.8493307,0.09581789,0.02239931,0.0006419697],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00492168,0.0000127941,0.9484872,0.001289377,0.00005841672,0.0002044094,5.45133e-7,0.0007750203,0.04425052],"genre_scores_gemma":[0.01903963,0.00001530184,0.9732164,0.0006777667,0.00004871799,0.00001807197,0.000002651165,0.000006493767,0.006975014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4462751,"threshold_uncertainty_score":0.3255709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04298823515020382,"score_gpt":0.3267948454851618,"score_spread":0.283806610334958,"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."}}