{"id":"W2160841154","doi":"10.1109/cvpr.2004.370","title":"Integrating Region and Boundary Information for Improved Spatial Coherencein Object Tracking","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer vision; Artificial intelligence; Boundary (topology); Computer science; Coherence (philosophical gambling strategy); Motion estimation; Active contour model; Motion field; Motion (physics); Object (grammar); Tracking (education); Parametric statistics; Mathematics; Image segmentation; Image (mathematics)","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":[0.001437948,0.0009020623,0.001302981,0.001672586,0.000597523,0.001347085,0.00155432,0.001424718,0.001450554],"category_scores_gemma":[0.004336212,0.001125542,0.0008361457,0.001262869,0.0007115154,0.003899789,0.002061948,0.001342642,0.0005909813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006512173,"about_ca_system_score_gemma":0.0008738459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00249224,"about_ca_topic_score_gemma":0.002897182,"domain_scores_codex":[0.9990342,0.0001910225,0.00004584361,0.0002185719,0.0004198243,0.00009063126],"domain_scores_gemma":[0.9986935,0.0005726643,0.0002071017,0.0002250852,0.0002420848,0.00005958149],"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":[0.0002588459,0.0001254144,0.001489707,0.0002398982,0.0001121086,0.0001870568,0.0005617442,0.1424655,0.2405129,0.02803241,0.001900902,0.5841136],"study_design_scores_gemma":[0.00002830031,0.0000946513,0.001197256,0.0000272366,0.00004379586,0.0001931629,0.00002844117,0.9355378,0.04631014,0.01040332,0.006084728,0.00005126821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004991073,0.0001669104,0.9942183,0.00002761702,0.00001008185,0.00001134909,0.00001387305,0.0003283463,0.0002324621],"genre_scores_gemma":[0.1007154,0.0002909247,0.8975711,0.00004914794,0.00004431111,0.00005657595,0.0001581525,0.0002926657,0.0008218288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00249224,"threshold_uncertainty_score":0.007604659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01337463685003124,"score_gpt":0.2683157888988477,"score_spread":0.2549411520488164,"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."}}