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1c166a71577871086d8e · Accurate and Interpretable Classification of Microspectroscopy Pixels Using Artificial Neural Networks

Accurate and Interpretable Classification of Microspectroscopy Pixels Using Artificial Neural Networks

This paper addresses the problem of classifying materials from microspectroscopical images at each pixel. The challenges lie in identifying discriminatory spectral features and obtaining accurate and interpretable models relating spectra and class labels

Официальный источник · 2017-01-06

Полные импортированные сведения карточки
{
  "title_en": "Accurate and Interpretable Classification of Microspectroscopy Pixels Using Artificial Neural Networks",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2017-01-06",
  "date_original": "January 6, 2017",
  "url": "https://www.nist.gov/publications/accurate-and-interpretable-classification-microspectroscopy-pixels-using-artificial",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Petru S. Manescu , Young Jong Lee , Charles Camp , Marcus T. Cicerone , Mary C. Brady , Peter Bajcsy",
  "abstract_excerpt_en": "This paper addresses the problem of classifying materials from microspectroscopical images at each pixel. The challenges lie in identifying discriminatory spectral features and obtaining accurate and interpretable models relating spectra and class labels",
  "id": "1c166a71577871086d8e",
  "sources": [
    "NIST Publications"
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  "source_records": [
    {
      "title_en": "Accurate and Interpretable Classification of Microspectroscopy Pixels Using Artificial Neural Networks",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2017-01-06",
      "date_original": "January 6, 2017",
      "url": "https://www.nist.gov/publications/accurate-and-interpretable-classification-microspectroscopy-pixels-using-artificial",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Petru S. Manescu , Young Jong Lee , Charles Camp , Marcus T. Cicerone , Mary C. Brady , Peter Bajcsy",
      "abstract_excerpt_en": "This paper addresses the problem of classifying materials from microspectroscopical images at each pixel. The challenges lie in identifying discriminatory spectral features and obtaining accurate and interpretable models relating spectra and class labels"
    }
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}