{"id":"W2162011993","doi":"10.1109/tip.2008.921985","title":"Robust Global Motion Estimation Oriented to Video Object Segmentation","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer vision; Motion estimation; Outlier; Computer science; Quarter-pixel motion; Motion compensation; Segmentation; Block-matching algorithm; Image segmentation; Coding (social sciences); Video tracking; Pattern recognition (psychology); Object (grammar); 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":[0.0005402683,0.000619409,0.0008533425,0.001041319,0.0001547004,0.0003973584,0.0008188343,0.000676059,0.0009476447],"category_scores_gemma":[0.001836005,0.0003316114,0.0005090495,0.0007379928,0.0004283279,0.0008987114,0.0005338172,0.0006144933,0.0004093418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003191458,"about_ca_system_score_gemma":0.0003444921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001307345,"about_ca_topic_score_gemma":0.001348273,"domain_scores_codex":[0.9994757,0.00008928405,0.00002552799,0.0001334343,0.000236594,0.00003952985],"domain_scores_gemma":[0.9994783,0.0001706004,0.00009092787,0.00009809908,0.0001416069,0.00002041236],"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":[0.0002244795,0.00004705287,0.0008089555,0.0001591218,0.00007936134,0.0001179903,0.0001026887,0.09779876,0.2617706,0.007242556,0.001429789,0.6302187],"study_design_scores_gemma":[0.00001894819,0.0001059319,0.001881091,0.00001611648,0.00003733977,0.0003140418,0.00002536032,0.8808088,0.108988,0.003280737,0.004482328,0.00004136902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005805989,0.0002271588,0.9931798,0.00002941197,0.00001172452,0.00001734522,0.00001602577,0.0003640223,0.0003485682],"genre_scores_gemma":[0.155319,0.0004982604,0.8416996,0.00006120977,0.00005283894,0.00004701457,0.0001846342,0.0001791207,0.001958347],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001307345,"threshold_uncertainty_score":0.003170192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02448399621604564,"score_gpt":0.2938254174922161,"score_spread":0.2693414212761704,"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."}}