{"id":"W2143712888","doi":"10.1109/tip.2008.2006455","title":"Segmentation of Tracking Sequences Using Dynamically Updated Adaptive Learning","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Artificial intelligence; Segmentation; Scale-space segmentation; Computer science; Computer vision; Segmentation-based object categorization; Image segmentation; Motion estimation; Kalman filter; Pattern recognition (psychology); Tracking (education); Sequence (biology)","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":[0.001162169,0.0006011454,0.000902254,0.001390712,0.0005552724,0.00105478,0.001209428,0.001275836,0.00108435],"category_scores_gemma":[0.003498845,0.0006622846,0.0008054329,0.001087926,0.0009531135,0.001436797,0.000921954,0.0009028479,0.0004260771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247651,"about_ca_system_score_gemma":0.001029328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00593652,"about_ca_topic_score_gemma":0.006591196,"domain_scores_codex":[0.9993865,0.0001416425,0.00003962937,0.0002129281,0.0001676761,0.00005155736],"domain_scores_gemma":[0.998975,0.0005075601,0.0001804045,0.0001211144,0.00017237,0.00004355067],"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":[0.0002185612,0.00005685763,0.0008905776,0.00009208929,0.00006576268,0.0001013208,0.0002512324,0.6671268,0.0207193,0.01339663,0.0008992166,0.2961818],"study_design_scores_gemma":[0.000006034091,0.00001684763,0.000181972,0.000005851427,0.00000635047,0.00002197295,0.000005482556,0.9941978,0.002062221,0.002956392,0.0005311518,0.000007913876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005610512,0.00008954878,0.993507,0.00003509444,0.00001376309,0.00001945375,0.000008997385,0.0002807265,0.0004348487],"genre_scores_gemma":[0.3115801,0.0002943645,0.685153,0.0001406496,0.00008935003,0.0001506676,0.0002007106,0.0001358813,0.002255291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00593652,"threshold_uncertainty_score":0.01180398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03371417843145297,"score_gpt":0.3037609229216797,"score_spread":0.2700467444902268,"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."}}