{"id":"W2211248819","doi":"10.1007/978-3-642-15352-5","title":"Variational and Level Set Methods in Image Segmentation","year":2010,"lang":"en","type":"book","venue":"Springer topics in signal processing","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":126,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Image (mathematics); Set (abstract data type); Artificial intelligence; Level set (data structures); Image segmentation; Computer science; Segmentation; Computer vision; Level set method; Pattern recognition (psychology); 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":[0.0006885087,0.0009760704,0.001913442,0.0013975,0.0003242803,0.001607079,0.001500871,0.001639941,0.007129168],"category_scores_gemma":[0.001422383,0.0009540507,0.001119589,0.002323684,0.001375564,0.001788316,0.001105173,0.00190471,0.002870958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008434922,"about_ca_system_score_gemma":0.0007569062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003377582,"about_ca_topic_score_gemma":0.003862815,"domain_scores_codex":[0.9995694,0.00008741784,0.00002114878,0.00008421632,0.0002170277,0.00002091701],"domain_scores_gemma":[0.9995937,0.0002297446,0.0000168038,0.00005065027,0.0000879124,0.00002118541],"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":[0.00003846957,0.00004761475,0.0001461973,0.0007797793,0.0001279577,0.00009064355,0.0001174268,0.1961967,0.009031693,0.3053118,0.04488125,0.4432305],"study_design_scores_gemma":[0.0000109172,0.00002906907,0.0004207262,0.00008370275,0.00003748342,0.0002042885,0.00002475769,0.6304963,0.002252799,0.2893318,0.07706081,0.0000473833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00114341,0.01835517,0.9617818,0.0005090663,0.0006470124,0.00003375579,0.0001339858,0.0005265067,0.0168693],"genre_scores_gemma":[0.06048941,0.03267173,0.781197,0.0004835359,0.002379843,0.0001946821,0.0006846383,0.001536526,0.1203627],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007129168,"threshold_uncertainty_score":0.02384943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03772093639842772,"score_gpt":0.33998836867932,"score_spread":0.3022674322808923,"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."}}