Automatic Diatom Identification
eBook - PDF

Automatic Diatom Identification

  1. 328 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Automatic Diatom Identification

About this book

This is the first book to deal with automatic diatom identification. It provides the necessary background information concerning diatom research, useful for both diatomists and non-diatomists. It deals with the development of electronic databases, image preprocessing, automatic contour extraction, the application of existing contour and ornamentation features and the development of new ones, as well as the application of different classifiers (neural networks, decision trees, etc.). These are tested using two image sets: (i) a very difficult set of Sellaphora pupula with 6 demes and 120 images; (ii) a mixed genera set with 37 taxa and approximately 800 images. The results are excellent, and recognition rates well above 90% have been achieved on both sets. The results are compared with identification rates obtained by human experts. One chapter of the book deals with automatic image capture, i.e. microscope slide scanning at different resolutions using a motorized microscope stage, autofocusing, multifocus fusion, and particle screening to select only diatoms and to reject debris. This book is the final scientific report of the European ADIAC project (Automatic Diatom Identification and Classification), and it lists the web-sites with the created public databases and an identification demo.

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Yes, you can access Automatic Diatom Identification by Micha M Bayer, Hans Du Buf in PDF and/or ePUB format, as well as other popular books in Computer Science & Optical Data Processing. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Contents
  2. Preface
  3. Acknowledgments
  4. Authors' Affiliations
  5. Chapter 1 Introduction to ADIAC and This Book
  6. Chapter 2 Diatoms: Organism and Image
  7. Chapter 3 Diatom Applications
  8. Chapter 4 ADIAC Imaging Techniques and Databases
  9. Chapter 5 Human Error and Quality Assurance in Diatom Analysis
  10. Chapter 6 Contour Extraction
  11. Chapter 7 Identification Using Classical and New Features in Combination with Decision Tree Ensembles
  12. Chapter 8 Identification by Curvature of Convex and Concave Segments
  13. Chapter 9 Identification by Contour Profiling and Legendre Polynomials
  14. Chapter 10 Identification by Gabor Features
  15. Chapter 11 Identification by Mathematical Morphology
  16. Chapter 12 Mixed-Method Identifications
  17. Chapter 13 Automatic Slide Scanning
  18. Chapter 14 ADIAC Achievements and Future Work
  19. Appendix: The Mixed Genera Data Set