{"id":"W2383079352","doi":"","title":"Optical Correlation Detection and Identification of Low Contrast Targets Under Cluttered Background","year":2015,"lang":"en","type":"article","venue":"Bandaoti guangdian","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Artificial intelligence; Contrast (vision); Computer vision; Computer science; Correlation; Histogram equalization; Pattern recognition (psychology); Histogram; Optical correlator; Filter (signal processing); Tracking (education); Identification (biology); Optics; Mathematics; Image (mathematics); Fourier transform; Physics","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":[0.0003760393,0.0003755265,0.0003414296,0.0008324129,0.0003528523,0.0004362487,0.0003592909,0.0004612588,0.0007575167],"category_scores_gemma":[0.0008131632,0.000243129,0.0002323753,0.0008190562,0.0003906832,0.0007147429,0.0004099141,0.0003038342,0.0002244288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003423754,"about_ca_system_score_gemma":0.000698702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001286796,"about_ca_topic_score_gemma":0.002096906,"domain_scores_codex":[0.9995864,0.00005427995,0.00001359222,0.0001078169,0.0001993422,0.00003856741],"domain_scores_gemma":[0.9997384,0.00007300957,0.00004597793,0.000030571,0.00009681519,0.00001527199],"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":[0.0002843791,0.000171049,0.008042415,0.0002928599,0.00007893579,0.0003304088,0.0002336989,0.01960156,0.561833,0.01079653,0.001694839,0.3966403],"study_design_scores_gemma":[0.000035376,0.0002461232,0.01173904,0.00001815142,0.00009163304,0.001064132,0.0001154927,0.5171854,0.4608184,0.003357439,0.005258679,0.00007004722],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2045401,0.0008287042,0.7892007,0.0001015705,0.00006315609,0.00003833447,0.00004439261,0.0006239569,0.004559024],"genre_scores_gemma":[0.7956628,0.0004882129,0.1997817,0.00006608868,0.00003050045,0.00004053744,0.00009071315,0.00003996465,0.00379946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001286796,"threshold_uncertainty_score":0.002558649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03867056318747378,"score_gpt":0.2723486803569351,"score_spread":0.2336781171694613,"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."}}