b9562983618716ee780a · Validating the quality of millimeter-wave images input to deep-learning-based threat detection systems

{
  "title_en": "Validating the quality of millimeter-wave images input to deep-learning-based threat detection systems",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2021-04-12",
  "date_original": "April 12, 2021",
  "url": "https://www.nist.gov/publications/validating-quality-millimeter-wave-images-input-deep-learning-based-threat-detection",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Jack Leigh Glover , Praful Gupta, Marius B. Facktor, Alan C. Bovik",
  "abstract_excerpt_en": "Deep-learning-based automatic image decision systems are increasingly being relied on to interpret imagery that was previously only viewed and analyzed by humans. Here, we present a real-time method of validating the quality of images input to image",
  "id": "b9562983618716ee780a",
  "sources": [
    "NIST Publications"
  ],
  "source_records": [
    {
      "title_en": "Validating the quality of millimeter-wave images input to deep-learning-based threat detection systems",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2021-04-12",
      "date_original": "April 12, 2021",
      "url": "https://www.nist.gov/publications/validating-quality-millimeter-wave-images-input-deep-learning-based-threat-detection",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Jack Leigh Glover , Praful Gupta, Marius B. Facktor, Alan C. Bovik",
      "abstract_excerpt_en": "Deep-learning-based automatic image decision systems are increasingly being relied on to interpret imagery that was previously only viewed and analyzed by humans. Here, we present a real-time method of validating the quality of images input to image"
    }
  ],
  "translation_state": "none"
}