CCO - 741 - Digital Image Processing

Total of Credits: 8
Hours for Theoretical Classes: 60
Hours for Exercises or Seminars: 60

Objective

Upon successful completion of this course, students should be able to: apply techniques for enhancing and segmenting digital images; pre-process images for the application of machine learning algorithms; identify the most suitable image processing techniques depending on the type of image to be processed.

Catalog Description

  • Biological and artificial vision.
  • Overview of the main steps of an image processing system.
  • Description of the main techniques for histogram processing.
  • Spatial filtering (linear and non-linear filters).
  • Filtering in the frequency domain.
  • Multiscale image processing.
  • Morphological processing.
  • Techniques for image description and representation.
  • Image classification and segmentation.

Main bibliography

  1. R. C. Gonzalez e R. E. Woods, “Digital Image Processing” (3rd Edition), Prentice-Hall, 2008.
  2. A. K. Jain, “Fundamentals of digital image processing”, Prentice-Hall, 1989.
  3. H. Pedrini e W. Robson, “Análise de imagens digitais: princípios, algoritmos e aplicações”, Thomson Learning, 2008.

Complementary bibliography

  1. D. A. Forsyth and J. Ponce, "Computer Vision: A Modern Approach", Prentice Hall, 2003.
  2. R. Szeliski, “Computer Vision: Algorithms and Applications”, Springer, 2010 (http://szeliski.org/Book/).
  3. M. Nixon, A. S. Aguado, “Feature Extraction & Image Processing for Computer Vision”, (2nd Edition), Academic Press, 2008.
  4. Openheim, A. V. and Schafer, R. W., Discrete-Time Signal Processing, Prentice-Hall, 1989.
  5. Proakis, J. G. and Manolakis, D. G., Digital Signal Processing: Principles, Algorithms and Applications, MacMIllan, 1992.