⌕ Поиск по атласу

NIST · ИТ · c8011ebe93ab498a21c7

c8011ebe93ab498a21c7 · Approaches to Training Multi-Class Semantic Image Segmentation of Damage in Concrete

Approaches to Training Multi-Class Semantic Image Segmentation of Damage in Concrete

This paper addresses the problem of creating a large quantity of high-quality training image segmentation masks from scanning electron microscopy (SEM) images of concrete samples that exhibit progressive amounts of degradation resulting from alkali-silica

Официальный источник · 2020-05-14

Полные импортированные сведения карточки
{
  "title_en": "Approaches to Training Multi-Class Semantic Image Segmentation of Damage in Concrete",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2020-05-14",
  "date_original": "May 14, 2020",
  "url": "https://www.nist.gov/publications/approaches-training-multi-class-semantic-image-segmentation-damage-concrete",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Peter Bajcsy , Steven B. Feldman , Michael P. Majurski , Kenneth A. Snyder , Mary C. Brady",
  "abstract_excerpt_en": "This paper addresses the problem of creating a large quantity of high-quality training image segmentation masks from scanning electron microscopy (SEM) images of concrete samples that exhibit progressive amounts of degradation resulting from alkali-silica",
  "id": "c8011ebe93ab498a21c7",
  "sources": [
    "NIST Publications"
  ],
  "source_records": [
    {
      "title_en": "Approaches to Training Multi-Class Semantic Image Segmentation of Damage in Concrete",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2020-05-14",
      "date_original": "May 14, 2020",
      "url": "https://www.nist.gov/publications/approaches-training-multi-class-semantic-image-segmentation-damage-concrete",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Peter Bajcsy , Steven B. Feldman , Michael P. Majurski , Kenneth A. Snyder , Mary C. Brady",
      "abstract_excerpt_en": "This paper addresses the problem of creating a large quantity of high-quality training image segmentation masks from scanning electron microscopy (SEM) images of concrete samples that exhibit progressive amounts of degradation resulting from alkali-silica"
    }
  ],
  "translation_state": "none"
}