{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003346719,0.00141478,0.002499653,0.002822644,0.001054702,0.001467964,0.002956829,0.002886273,0.002047506],"category_scores_gemma":[0.01320844,0.001447146,0.00201918,0.003079991,0.001618876,0.002007605,0.002694474,0.002206954,0.0009903479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002158113,"about_ca_system_score_gemma":0.001510164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006947351,"about_ca_topic_score_gemma":0.005411413,"domain_scores_codex":[0.9982125,0.0006861401,0.0001395645,0.0004721437,0.0003683227,0.0001214044],"domain_scores_gemma":[0.9950114,0.002539388,0.000449422,0.0007795122,0.001061779,0.0001584906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007545317,0.0000240164,0.0004027317,0.00008492114,0.00007969792,0.00005831342,0.0001299764,0.9221762,0.001709849,0.01412397,0.001071602,0.06006321],"study_design_scores_gemma":[0.000003057077,0.00000709547,0.00007974331,0.000004882978,0.000002941906,0.000008076547,0.000008002706,0.9935704,0.0004388336,0.005526578,0.0003434925,0.00000685449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002463554,0.0001205268,0.9968367,0.00005482279,0.00002094327,0.00002666084,0.00003682636,0.0001961498,0.0002437785],"genre_scores_gemma":[0.1824837,0.0004338837,0.8112122,0.0001524374,0.0001662037,0.0004205818,0.00105911,0.0003360004,0.003735893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006947351,"threshold_uncertainty_score":0.0176993,"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."}}