{"id":"W1983480533","doi":"10.5244/c.18.26","title":"EM Clustering of Incomplete Data Applied to Motion Segmentation","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Segmentation; Cluster analysis; Optical flow; Artificial intelligence; Pixel; Image segmentation; Computer science; Motion estimation; Computer vision; Scale-space segmentation; Pattern recognition (psychology); Motion (physics); Similarity (geometry); Algorithm; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009188849,0.00004461554,0.00005809438,0.00005841175,0.0000340458,0.0000356436,0.0005086202,0.000007404811,0.000007825747],"category_scores_gemma":[0.000007979911,0.00004117188,0.00000709751,0.0001868939,0.000005140515,0.0004985182,0.000600928,0.00002414977,0.00003469439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002327964,"about_ca_system_score_gemma":0.000009324397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000125173,"about_ca_topic_score_gemma":0.00001406135,"domain_scores_codex":[0.9994559,0.00000488215,0.0001263281,0.0002055701,0.00012444,0.00008283419],"domain_scores_gemma":[0.9994189,0.000007985305,0.00003592661,0.0004806909,0.00001970336,0.00003685593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004093902,0.00002932233,0.00002362184,0.0000114161,0.000002955195,7.470168e-7,0.0007995564,0.02826759,0.09754522,0.01566089,0.00007736747,0.8575772],"study_design_scores_gemma":[0.001111538,0.00006278374,0.003269908,0.00004442884,0.000003029005,0.000007134143,0.0004872985,0.9050257,0.08109172,0.007852178,0.0007998208,0.0002444856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002251477,0.000002456597,0.9958268,0.0004180979,0.00007722106,0.00009932798,0.00000125741,0.00006716228,0.001256133],"genre_scores_gemma":[0.4234175,6.185965e-7,0.5761697,0.0003814226,0.000008298014,0.000001273002,0.000005328582,0.000001907443,0.00001392537],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8767581,"threshold_uncertainty_score":0.1678941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05013465047915944,"score_gpt":0.3197173493775797,"score_spread":0.2695826988984202,"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."}}