{"id":"W2148049896","doi":"10.1109/tcsvt.2002.806811","title":"Object-based video coding by global-to-local motion segmentation","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial intelligence; Computer vision; Segmentation; Computer science; Coding (social sciences); Image segmentation; Motion estimation; Scale-space segmentation; Coding tree unit; Data compression; Pattern recognition (psychology); Mathematics; Algorithm; Decoding methods","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":[0.0003356158,0.0004538692,0.0003885201,0.0008350596,0.0001987199,0.0003995432,0.0007396385,0.0004156001,0.001089126],"category_scores_gemma":[0.0007209026,0.0001617833,0.0003045474,0.0008692436,0.0004326024,0.0008101155,0.0005178637,0.0005397646,0.0006022534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003332286,"about_ca_system_score_gemma":0.0003594463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000998831,"about_ca_topic_score_gemma":0.001025836,"domain_scores_codex":[0.9997939,0.00002792365,0.00001093093,0.00002483377,0.000118949,0.00002339131],"domain_scores_gemma":[0.9998063,0.00005413116,0.00002338042,0.00005058882,0.00005504299,0.00001054767],"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":[0.0002442806,0.00008473603,0.0004425611,0.0001761956,0.0000262665,0.0001216082,0.0001538686,0.06954928,0.2886532,0.05694791,0.002259367,0.5813407],"study_design_scores_gemma":[0.0000425714,0.0001675805,0.0009152591,0.00003808469,0.00002790157,0.000432025,0.00001986561,0.7777241,0.1921086,0.0168619,0.01162608,0.0000360259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009552526,0.0002237099,0.9886008,0.0000291341,0.00001826158,0.00004527607,0.00003240793,0.0003544,0.001143445],"genre_scores_gemma":[0.1257365,0.0003768532,0.8713712,0.00005924354,0.00004182032,0.0001178874,0.0002366742,0.0001095038,0.001950328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001089126,"threshold_uncertainty_score":0.003643513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02113973582649241,"score_gpt":0.2653542794674226,"score_spread":0.2442145436409301,"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."}}