Digital image processing and analysis

Course objectives

The course provides knowledge in theory and applications of digital image processing and analysis.

Elements of human visual system. Two-dimensional (2-D) sequences. Linear 2-D system. 2-D convolution. Sampling and quantization. 2-D transforms.

Image enhancement in spatial domain.

Image histogram operations. Histogram equalization and specification.

Homomorfic filtering. Median filter.

Image enhanement in the frequency domain. Image restoration.

Inverse and pseudoinverse filtering. Wiener filter.

Geometric image transformations. Color image representation and processing. Image feature extraction.

Principal component analysis. Edge detection. Gradient and compass operators. Object boundary detection.

Enrolment requirements and/or entry competences required for the course

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Learning outcomes at the level of the programme to which the course contributes

  • Apply theoretical knowledge of the fundamentals of the six core disciplines and their relationship within cognitive science.
  • Critically evaluate cognitive science findings and synthesize information to be employed in a collaborative professional environment.
  • Participate in data-driven innovation projects and apply appropriate data science tools.
  • Apply AI tools in concrete tasks and practical contexts.

Course content (syllabus)

  1. Light and EM spectrum, Human visual system.
  2. Image sampling and quantization, Discrete geometry.
  3. Image sensors, Application areas.
  4. 2D linear systems.
  5. 2D linear systems, Basic image processing operations.
  6. 2D transforms.
  7. 2D transforms, Geometric transforms.
  8. Digital image representation, Compression.
  9. Gray-level transformations, Histogram operations.
  10. Spatial filtering, Median filtering, Homomorphic filtering.
  11. Image degradation models, Noise models, Inverse filter and pseudoinverse filter, 2D Wiener filtering.
  12. Color representation, Color models, Color spaces, Color transformations.
  13. Edge and corner detection.
  14. Edge and corner detection.
  15. Scale space, Orientation Histogram, Hessian operator, Curvature estimation, Detection of discontinuities.

Student responsibilities

The student is required to attend lectures and seminar, to prepare and to defend a seminar paper, and to pass both written and oral exams.

Required literature

  • Gonzalez, Woods. Digital Image Processing, 3rd Ed., Pearson, 2007

Optional literature

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