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Filter Design for Image Decomposition and Applications to Forensics

In: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging

Author

Listed:
  • Robin Richter

    (University of Göttingen, Felix-Bernstein-Institute for Mathematical Statistics in the Biosciences)

  • Duy H. Thai

    (Colorado State University, Department of Mathematics)

  • Carsten Gottschlich

    (University of Göttingen, Institute for Mathematical Stochastics)

  • Stephan F. Huckemann

    (University of Göttingen, Felix-Bernstein-Institute for Mathematical Statistics in the Biosciences)

Abstract

Employing image filters in image processing applications, essentially matrix convolution operators, has been an active field of research since a long time, and it is so very much still today. In the first part, we give a brief overview of imaging methods with emphasis on applications in fingerprint recognition and shoeprint forensics. In the second part, we propose a generalized discrete scheme for image decomposition that encompasses many of the existing methods. Due to its generality, it has the potential to learn, for specific use cases, a highly flexible set of imaging filters that are related to one another by rather general conditions.

Suggested Citation

  • Robin Richter & Duy H. Thai & Carsten Gottschlich & Stephan F. Huckemann, 2023. "Filter Design for Image Decomposition and Applications to Forensics," Springer Books, in: Ke Chen & Carola-Bibiane Schönlieb & Xue-Cheng Tai & Laurent Younes (ed.), Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, chapter 32, pages 1155-1182, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-98661-2_92
    DOI: 10.1007/978-3-030-98661-2_92
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