{"id":"W2235559271","doi":"10.1007/978-3-319-24261-3_11","title":"Unsupervised Motion Segmentation Using Metric Embedding of Features","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Embedding; Computer vision; Motion (physics); Metric (unit); Image segmentation; A priori and a posteriori; Prior probability; Pattern recognition (psychology)","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":[0.0003398844,0.001011006,0.001303564,0.001487852,0.0003137998,0.0009052318,0.001249416,0.0008139593,0.001652569],"category_scores_gemma":[0.001129199,0.0005909072,0.0008952466,0.001928159,0.0005747635,0.001143276,0.001105759,0.0008686144,0.001303358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005571441,"about_ca_system_score_gemma":0.0006796417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003590633,"about_ca_topic_score_gemma":0.005442317,"domain_scores_codex":[0.9996157,0.00005831017,0.00002179382,0.0001598968,0.00009113206,0.00005316041],"domain_scores_gemma":[0.9995686,0.0001175457,0.00005745329,0.00009803911,0.0001279901,0.00003039109],"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":[0.0002187613,0.00008858976,0.0008442876,0.0001667451,0.00008573796,0.0000657939,0.000135818,0.09243432,0.0774281,0.01126882,0.005247178,0.8120157],"study_design_scores_gemma":[0.000009797725,0.00007184612,0.0009954543,0.00002211971,0.0000179461,0.00009563422,0.00003311065,0.9710256,0.01487306,0.009577884,0.003259647,0.00001788794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009349702,0.0002372407,0.9886147,0.00003903189,0.00002795763,0.00003594805,0.0001157861,0.0008455386,0.0007341853],"genre_scores_gemma":[0.1845522,0.0005071874,0.8079771,0.00006174561,0.00007092177,0.0001181428,0.001679919,0.0006078935,0.00442485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003590633,"threshold_uncertainty_score":0.007139504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05885663374578947,"score_gpt":0.3361807769074182,"score_spread":0.2773241431616287,"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."}}