Digital Image Processing and Analysis

Learning Outcomes

  1. Define and describe concepts of image and video processing and analysis
  2. List examples of digital image and video processing applications
  3. Explain methods for image processing and analysis
  4. Analyze a practical image processing and analysis problem
  5. Combine acquired knowledge and propose a solution to the given problem
  6. Evaluate a practical solution to an image processing and analysis problem

Forms of Teaching


The lectures present theoretical concepts and algorithms followed by concrete examples.


Solving typical tasks related to the lecture material


Solving practical problems related to the subject matter of the lectures

Week by Week Schedule

  1. Light and EM spectrum; Human visual system; Image sampling and quantization; Discrete geometry; Image sensors; Application areas.
  2. 2D linear systems; Basic image processing operations.
  3. 2D Fourier transform; 2D discrete cosine transform; 2D discrete sine transform; 2D discrete wavelet transform; Karhunen-Loeve transform; Interpolation techniques; Geometric transforms.
  4. Gray-level transformations; Histogram operations; Spatial filtering; Median filtering; Homomorphic filtering.
  5. Image degradation models; Noise models; Inverse filter and pseudoinverse filter; 2D Wiener filtering.
  6. Color representation; Color models; Color spaces; Color transformations.
  7. Application areas; Project.
  8. Midterm exam.
  9. Edge and corner detection; Scale space; Orientation Histogram; Hessian operator; Curvature estimation; Detection of discontinuities.
  10. Image segmentation; Boundary detection; Thresholding; Region growing; Watershed segmentation; Segmentation by motion; Mean shift cllustering; Graph-based methods; Hough transform.
  11. Shape analysis; Region representation; Boundary descriptors; Region descriptors; Shape description methods; Classification of shape; Texture features; Texture analysis; Texture analysis; Shape analysis; Dilation and erosion; Opening and closing; Thinning and skeletons; Grayscale morphology.
  12. Motion analysis.
  13. Project.
  14. Project.
  15. Final exam.

Study Programmes

University undergraduate
Computing (study)
Elective Courses (6. semester)
Electrical Engineering and Information Technology (study)
Elective Courses (6. semester)


(.), S. Loncaric. M. Subasic. Lectures Notes in Digital Image Processing and Analysis,
(.), Gonzalez, Woods. Digital Image Processing, 3rd Ed., Pearson, 2007,


ID 183463
  Summer semester
L3 English Level
L1 e-Learning
24 Lectures
15 Exercises
13 Laboratory exercises
0 Project laboratory

Grading System

Very Good