{"id":"W2604709832","doi":"10.1109/iccv.2017.295","title":"AMAT: Medial Axis Transform for Natural Images","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medial axis; Computer science; Generalization; Artificial intelligence; Point (geometry); Pixel; Computer vision; Cluster analysis; Pattern recognition (psychology); Scale (ratio); Image (mathematics); Code (set theory); Set (abstract data type); Mathematics; Cartography; Geography; Geometry","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":[0.0001511904,0.0001059887,0.0001293149,0.00003709851,0.0003729046,0.0003388744,0.001289287,0.00004106389,0.00001157962],"category_scores_gemma":[0.0001568444,0.00007973933,0.0000980173,0.00003628408,0.00008087952,0.001813471,0.0001254391,0.00008466113,0.00001242857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001520209,"about_ca_system_score_gemma":0.00002406622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001435045,"about_ca_topic_score_gemma":0.000009720085,"domain_scores_codex":[0.9992367,0.000005252406,0.0001280637,0.0002500478,0.0001412741,0.0002386754],"domain_scores_gemma":[0.9990749,0.00006782138,0.00006263237,0.0006450732,0.00009183631,0.00005775057],"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":[0.00001805014,0.00001932276,0.00002936431,0.0000161646,0.000007455258,0.00000661043,0.0001077192,7.898854e-8,0.007145799,0.02863936,0.007595906,0.9564142],"study_design_scores_gemma":[0.0004250563,0.000130668,0.0008464985,0.00001143316,0.00000367184,0.000007940513,0.000006190488,0.001369708,0.8897484,0.06382277,0.04342481,0.0002028904],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002611293,0.0001029515,0.984769,0.003471524,0.000373938,0.0002738183,0.000004596878,0.0003573638,0.01038568],"genre_scores_gemma":[0.5963007,0.00007601648,0.4000449,0.0003672373,0.0001617853,0.00003827047,0.000001943867,0.00000888596,0.003000278],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9562113,"threshold_uncertainty_score":0.3267774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0195209355380531,"score_gpt":0.3246988900197789,"score_spread":0.3051779544817257,"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."}}