{"id":"W2156928155","doi":"10.1109/tpami.2006.97","title":"Joint multiregion segmentation and parametric estimation of image motion by basis function representation and level set evolution","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Institut National de la Recherche Scientifique; Communications Research Centre Canada","funders":"","keywords":"Artificial intelligence; Segmentation; Scale-space segmentation; Motion estimation; Image segmentation; Mathematics; Segmentation-based object categorization; Computer vision; Pattern recognition (psychology); Motion field; Basis function; Range segmentation; Parametric statistics; Computer science; Algorithm; Mathematical analysis","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":[0.002224352,0.0006360791,0.001150918,0.0009890547,0.0004067249,0.001211822,0.001488162,0.0016416,0.000887104],"category_scores_gemma":[0.004200801,0.0007248199,0.001463843,0.0007477736,0.001482209,0.001780097,0.001495549,0.001226412,0.0002668325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232572,"about_ca_system_score_gemma":0.001437346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002429902,"about_ca_topic_score_gemma":0.002265955,"domain_scores_codex":[0.9992742,0.0002701214,0.00003179527,0.000128571,0.0002448916,0.00005038609],"domain_scores_gemma":[0.9990651,0.0005485077,0.00009836753,0.0001246632,0.0001208081,0.0000426134],"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":[0.00008052171,0.00004345133,0.0006454383,0.0001469725,0.0000788711,0.00008552302,0.000207445,0.7657599,0.02506884,0.1060086,0.0004238591,0.1014505],"study_design_scores_gemma":[0.000002654791,0.00001673938,0.00009063736,0.00000558907,0.000005091221,0.0000312624,0.000005761648,0.9894298,0.001715622,0.00814228,0.0005476855,0.00000685636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00216954,0.00005292573,0.9974928,0.00003830505,0.000004667115,0.00001063287,0.000003812168,0.00003370266,0.0001936373],"genre_scores_gemma":[0.1772405,0.0002216601,0.8194842,0.00004890153,0.00003398927,0.0001426368,0.00005562029,0.000171061,0.00260135],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002429902,"threshold_uncertainty_score":0.01176363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03222862345194479,"score_gpt":0.2936833373319199,"score_spread":0.2614547138799751,"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."}}