PristupačnostDigital Image Processing and Analysis
Data is displayed for the academic year: 2025./2026.
Lectures
Exercises
Laboratory exercises
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.
Prerequisites
A good background in mathematics including linear algebra, calculus, geometry, probability and statistics.
Study Programmes
University undergraduate
[FER3-EN] Computing - study
Elective Courses
(6. semester)
[FER3-EN] Electrical Engineering and Information Technology - study
Elective Courses
(6. semester)
University graduate
[FER3-EN] Data Science - profile
Recommended elective courses
(2. semester)
Learning Outcomes
- Define and describe the concepts of image processing and analysis
- Explain selected applications of digital image processing and analysis
- Apply image processing and analysis methods
- Analyze a practical image processing and analysis problem
- Combine the acquired knowledge and propose a solution to agiven problem
- Evaluate a practical solution to an image processing and analysis problem
Forms of Teaching
Lectures
The lectures present theoretical concepts and algorithms followed by concrete examples.
ExercisesSolving practical tasks related to the lectures.
LaboratoryUsing 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 % | ||||
Comment:
The threshold on the sum of the midterm and the final exam is 50%.
Week by Week Schedule
- Light and EM spectrum. Human visual system. Image sensors. Image sampling and quantization.
- Basic image processing operations. 2D linear systems.
- 2D transforms. Fourier transform. Discrete cosine and sine transforms. Karhunen-Loeve transform.
- Image enhancement. Gray-level intensity transformations. Histogram operations. Spatial filtering. Median filtering. Homomorphic filtering.
- Image degradation models. Noise models. Inverse filter and pseudoinverse filter. 2D Wiener filter.
- Image feature extraction. Histogram features. Texture features. Edge and corner detection.
- Image segmentation. Thresholding. Selecting the threshold. Boundary tracing. Split and merge.
- Midterm exam
- Hough transform. Neural networks for image segmentation.
- Shape analysis. Boundary descriptors. Region descriptors. Thinning and skeletonization.
- Motion analysis. Optical flow.
- Image registration. Geometrical transformations. Procrustes problem.
- Project
- Project
- Final exam
Literature
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
William K. Pratt (2013.), Introduction to Digital Image Processing, CRC Press
General
ID 268931
Summer semester
5 ECTS
L2 e-Learning
30 Lectures
0 Seminar
15 Exercises
15 Laboratory exercises
0 Project laboratory
0 Physical education excercises
Grading System
87 Excellent
75 Very Good
63 Good
51 Sufficient