Description

Thousands of high-resolution images are generated each day. Segmenting, classifying, and analyzing the contents of these images are the key steps in image understanding. This thesis focuses on image segmentation

Thousands of high-resolution images are generated each day. Segmenting, classifying, and analyzing the contents of these images are the key steps in image understanding. This thesis focuses on image segmentation and classification and its applications in synthetic, texture, natural, biomedical, and industrial images. A robust level-set-based multi-region and texture image segmentation approach is proposed in this thesis to tackle most of the challenges in the existing multi-region segmentation methods, including computational complexity and sensitivity to initialization.

Reuse Permissions
  • 6.32 MB application/pdf

    Download count: 0

    Details

    Contributors
    Date Created
    • 2010
    Resource Type
  • Text
  • Collections this item is in
    Note
    • Partial requirement for: Ph.D., Arizona State University, 2010
      Note type
      thesis
    • Includes bibliographical references (p. 142-159)
      Note type
      bibliography
    • Field of study: Electronic engineering

    Citation and reuse

    Statement of Responsibility

    by Asaad F. Said

    Machine-readable links