{"id":"W3178686803","doi":"","title":"Automatic segmentation of pupil using local histogram and standard deviation","year":2010,"lang":"en","type":"article","venue":"Figshare","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Histogram; Thresholding; Pupil; Artificial intelligence; Standard deviation; Robustness (evolution); Segmentation; Computer science; Computer vision; Pattern recognition (psychology); Balanced histogram thresholding; Image segmentation; Mathematics; Histogram matching; Statistics; 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":[0.0005761291,0.0003755028,0.0007821759,0.002742686,0.0003772018,0.0009712101,0.0008408012,0.0006216342,0.001948853],"category_scores_gemma":[0.001836244,0.000334096,0.0006333309,0.001203049,0.0003464932,0.001244332,0.0007957853,0.0005037339,0.0009294598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000383981,"about_ca_system_score_gemma":0.0004963978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001264386,"about_ca_topic_score_gemma":0.001319957,"domain_scores_codex":[0.9993306,0.00009910658,0.00004946006,0.0001994026,0.0002474386,0.00007403694],"domain_scores_gemma":[0.9990434,0.0003773177,0.0001539331,0.0001185819,0.0002699076,0.00003687221],"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":[0.0005195253,0.00008714866,0.005290269,0.0003306161,0.0001177833,0.000184198,0.000222373,0.008817825,0.220429,0.002646852,0.003410568,0.7579439],"study_design_scores_gemma":[0.0001258806,0.0004776295,0.0470066,0.0001030638,0.0001831617,0.002326202,0.0003010902,0.5495098,0.3801179,0.006330804,0.01332066,0.0001972569],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07635643,0.001144537,0.9156235,0.0001254216,0.00006219001,0.00007786928,0.0002547474,0.003764143,0.002591237],"genre_scores_gemma":[0.4996938,0.001054093,0.4952465,0.0001091186,0.0001044841,0.00013842,0.0007455891,0.0006022919,0.002305692],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002742686,"threshold_uncertainty_score":0.006519616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02415039394711304,"score_gpt":0.2729825791201931,"score_spread":0.24883218517308,"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."}}