{"id":"W2366291097","doi":"","title":"A Method of Infrared Image Segment Based on Mathematical Morphology","year":2006,"lang":"en","type":"article","venue":"Guidance and Fuze","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Mathematical morphology; Binary image; Image (mathematics); Artificial intelligence; Computer vision; Noise (video); Morphological gradient; Infrared; Character (mathematics); Mathematics; Binary number; Computer science; Morphology (biology); Pattern recognition (psychology); Image processing; Physics; Optics; Arithmetic; Geometry","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":[0.0002079342,0.00008245646,0.000146058,0.00005268912,0.00001865012,0.000004975553,0.00003390361,0.00004202545,0.0000678232],"category_scores_gemma":[0.00001960066,0.0000749192,0.00003068833,0.00008185355,0.00002580643,0.00003549238,0.000006203425,0.00006038852,0.000007682242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001506305,"about_ca_system_score_gemma":0.000004326227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001532623,"about_ca_topic_score_gemma":3.50529e-7,"domain_scores_codex":[0.9994921,0.00003156426,0.0001724956,0.00009444608,0.00009555051,0.0001138514],"domain_scores_gemma":[0.9997259,0.00007806302,0.00002386827,0.0001241563,0.00002325902,0.00002475725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007265083,0.00008260504,0.0003410967,0.0003372309,0.00001816799,0.00001040159,0.00005697763,0.03628697,0.9169005,0.001875646,0.001934731,0.04208305],"study_design_scores_gemma":[0.001690226,0.0002216434,0.005583161,0.0001121826,0.00003535246,0.00001555011,0.00004826226,0.1225932,0.8136051,0.045039,0.01069092,0.0003654213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00836384,0.0001841355,0.9715171,0.00001912114,0.00005668466,0.0000775844,0.000004095371,0.0000770635,0.01970032],"genre_scores_gemma":[0.05744505,0.00001002631,0.9421325,0.0000651068,0.00005373843,0.00002274764,0.000001881133,0.00001454158,0.0002544673],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1032954,"threshold_uncertainty_score":0.3055117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01510809633753707,"score_gpt":0.2769547084791922,"score_spread":0.2618466121416551,"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."}}