{"id":"W2809931234","doi":"10.3390/rs10071039","title":"A Level Set Method for Infrared Image Segmentation Using Global and Local Information","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"State Key Laboratory of Networking and Switching Technology; China Scholarship Council; Beijing University of Posts and Telecommunications; National Natural Science Foundation of China","keywords":"Level set (data structures); Computer science; Level set method; Artificial intelligence; Initialization; Computer vision; Signed distance function; Robustness (evolution); Image segmentation; Active contour model; Pixel; Segmentation; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000477494,0.0001020187,0.000109191,0.00007648957,0.0001560678,0.0002288496,0.0001099321,0.00005969671,0.000001805751],"category_scores_gemma":[0.000165239,0.0001045568,0.00002694786,0.0002421451,0.00009709701,0.001117479,0.0001158808,0.00004726857,0.000005454011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001309968,"about_ca_system_score_gemma":0.00007057694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000106593,"about_ca_topic_score_gemma":0.000007737298,"domain_scores_codex":[0.9990785,0.00008109795,0.0002549039,0.0001833017,0.0002180113,0.0001842301],"domain_scores_gemma":[0.9992844,0.00006131038,0.0001336554,0.0001878744,0.0002502865,0.00008247217],"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":[0.000008181389,0.000001576574,0.000001784505,0.00002263629,0.000006427145,0.000001064037,0.0006868466,0.000007470105,0.02243812,0.00008014731,0.0004946162,0.9762511],"study_design_scores_gemma":[0.0003438829,0.00005972564,0.00005758178,0.00004111017,0.000008547295,0.00008363496,0.0001667535,0.8939587,0.1011573,0.003842755,0.0001708091,0.0001091713],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003555777,0.000006113351,0.9954233,0.0001345588,0.0001438735,0.000310685,0.000008955049,0.0001557262,0.0002610339],"genre_scores_gemma":[0.006600574,0.000002120337,0.9921139,0.001182156,0.0000706384,5.521968e-8,0.0000153669,0.000004875139,0.0000103258],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.976142,"threshold_uncertainty_score":0.4263702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04803984980130425,"score_gpt":0.3681305397361291,"score_spread":0.3200906899348249,"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."}}