Digital Image Processing and Analysis

Course Description

Elements of the human visual system. Two-dimensional (2D) sequences. Linear 2D system. 2D convolution. Sampling and quantization. 2D transforms. Image enhancement in the spatial domain. Image histogram operations. Histogram equalization and specification. Homomorfic filtering. Median filter. Image enhancement in the frequency domain. Image restoration. Inverse and pseudoinverse filters. Wiener filter. Geometric image transformations. Representation and processing of color images. Image feature extraction. Principal component analysis. Edge detection. Gradient and compass operators. Object boundary detection. Image segmentation. Hough transform. Clustering methods. Texture segmentation. Optical flow. Applications in biomedicine, industrial visual quality control, and communications.

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 practical tasks related to the lectures.


Using a computer to process and analyze images.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 50 % 20 % 50 % 20 %
Seminar/Project 20 % 20 % 20 % 20 %
Mid Term Exam: Written 20 % 30 % 0 %
Final Exam: Written 20 % 30 %
Exam: Written 50 % 60 %

The threshold on the sum of the midterm and the final exam is 50%.

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
Elective Courses (6. semester)
Elective Courses (6. semester)
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Richard Szeliski (2017.), Computer Vision: Algorithms and Applications, Pearson
Rafael Gonzalez, Richard Woods (2017.), Digital Image Processing, Pearson
Bernd Jähne (2005.), Digital Image Processing, Springer

Laboratory exercises

For students


ID 229829
  Summer semester
L1 English Level
L2 e-Learning
30 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory

Grading System

87 Excellent
75 Very Good
63 Good
51 Sufficient