{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006700701,0.0005985395,0.0008011456,0.001548752,0.0005889598,0.00118548,0.001039049,0.0009158088,0.002773762],"category_scores_gemma":[0.001180107,0.0004949093,0.001040352,0.001099412,0.000783512,0.001821244,0.0006410195,0.0009551858,0.001259274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004543276,"about_ca_system_score_gemma":0.0007556594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008957407,"about_ca_topic_score_gemma":0.0007381394,"domain_scores_codex":[0.9991283,0.000106999,0.00005891377,0.0002021403,0.0004522373,0.00005134151],"domain_scores_gemma":[0.999414,0.0001332798,0.00005372032,0.0001183795,0.0002464251,0.00003428199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002030624,0.00007646774,0.0008329966,0.0003779016,0.00009474925,0.000212903,0.0003822224,0.01107876,0.3179228,0.02816698,0.003400382,0.6372507],"study_design_scores_gemma":[0.00009604757,0.0008931355,0.005193689,0.0000605463,0.0001956254,0.004595925,0.0001774997,0.5093374,0.3947136,0.01729824,0.06714606,0.0002922501],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003917801,0.0001620697,0.9942428,0.000041423,0.00004498569,0.00004951867,0.00001873826,0.0005888854,0.0009336982],"genre_scores_gemma":[0.05954954,0.0003821551,0.9355501,0.00005244017,0.00008910477,0.0001272438,0.0001257332,0.0001690104,0.003954775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002773762,"threshold_uncertainty_score":0.009279132,"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."}}