{"id":"W2096824499","doi":"10.1109/cvpr.2004.402","title":"Motion Segmentation by EM Clustering of Good Features","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Cluster analysis; Segmentation; Computer science; Artificial intelligence; Image segmentation; Noise (video); Affine transformation; Scale-space segmentation; Pattern recognition (psychology); Independence (probability theory); Computer vision; Algorithm; Mathematics; Image (mathematics); Statistics","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":[0.001154987,0.001148505,0.001270544,0.001588389,0.0006969426,0.001002616,0.001831791,0.001604098,0.001722439],"category_scores_gemma":[0.003676241,0.000879434,0.001064855,0.001590339,0.001099661,0.001566272,0.001360808,0.001226159,0.00133916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001110395,"about_ca_system_score_gemma":0.0009664678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003372329,"about_ca_topic_score_gemma":0.004230581,"domain_scores_codex":[0.9993862,0.0001067044,0.0000346655,0.0002368243,0.0001567654,0.00007875528],"domain_scores_gemma":[0.998914,0.0003523273,0.0001700983,0.0002559488,0.0002525899,0.00005509102],"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":[0.0002236092,0.000098598,0.001668583,0.0001255761,0.0001241882,0.0001589919,0.0002525104,0.6165401,0.03390469,0.01971184,0.004131137,0.3230601],"study_design_scores_gemma":[0.000009935435,0.00002217912,0.0002968337,0.000005689509,0.000007168411,0.00003593262,0.00001330949,0.9857448,0.004344069,0.008325105,0.001183063,0.00001208781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004844829,0.00003790322,0.9943691,0.0000272715,0.00000852422,0.00002355328,0.0000216928,0.0003598808,0.0003072473],"genre_scores_gemma":[0.09763083,0.00007639024,0.8996038,0.00006650668,0.00002880943,0.0001471036,0.0003809734,0.0003457825,0.001719891],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003372329,"threshold_uncertainty_score":0.008056462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00803322156187687,"score_gpt":0.2724701303554757,"score_spread":0.2644369087935989,"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."}}