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Digital Image Processing, 6 credits
Digital bildbehandling grundkurs, 6 hp
TSBB08
Main field of study
Computer Science and Engineering Electrical EngineeringCourse level
Second cycleCourse type
Programme courseExaminer
Maria MagnussonDirector of studies or equivalent
Klas NordbergEducation components
Preliminary scheduled hours: 62 hRecommended self-study hours: 98 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
Main field of study
Computer Science and Engineering, Electrical EngineeringCourse level
Second cycleAdvancement level
A1XCourse offered for
- Computer Science and Engineering, M Sc in Engineering
- Industrial Engineering and Management - International, M Sc in Engineering
- Industrial Engineering and Management, M Sc in Engineering
- Information Technology, M Sc in Engineering
- Computer Science and Software Engineering, M Sc in Engineering
- Applied Physics and Electrical Engineering - International, M Sc in Engineering
- Applied Physics and Electrical Engineering, M Sc in Engineering
- Biomedical Engineering, Master's programme
Entry requirements
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.
Prerequisites
1-D signal processing: deterministic signals, linear systems, convolution, continuous and discrete Fourier transform sampling and reconstruction, the sampling theorem, basic filters (low-pass, high-pass, and band-pass). Linear algebra: vector, matrix, determinant, scalar product, bases, the least square method. One- and multidimensional calculus. Programming in one of the following languages: C, C++, Java, Ada or Matlab.Intended learning outcomes
The course aims to provide basic knowledge in 2D signal processing and a systematic description about the classical methods and tools for digital image processing. This means that a student which has taken this course is expected to be able to:
- Describe basics regarding the generalization from 1-D to 2-D signal processing: Continuous and discrete Fourier transform with accompanying theorems, sampling and reconstruction, convolution, re-sampling and interpolation, scale space.
- Interpret the result of a 2-D Fourier transform of an image, such as what is a spatial frequency and be acquainted with the most common convolution kernels and describe their appearance in the spatial and Fourier domain, respectively.
- Describe most of the classical image processing methods in the course content, see below.
- Solve simple image processing problems using Matlab.
Course content
The lectures:
- Concepts and definitions. From 1-D to 2-D Fourier transform. Continuous and discrete Fourier transform, DFT, FFT. Sampling and reconstruction. Convolution and filtering, translation, scaling, derivative, rotation, and other linear operations on digital images.
- Convolution kernels in the spatial and Fourier domain, low-pass, derivative (sobel).
- Resampling and interpolation. Scale space.
- Color models. Color transformations. Color segmentation.
- Segmentation: Regional growing, watersheds, labeling. Operations on histogram. Thresholding: automatic, local and with hysteres.
- Binary image processing: Morphological operations, distance transform, connectivity preserving operations, feature extraction, chain code, polygon approximation and Fourier descriptors.
- Matched filters and pattern recognition. Edge detection with Sobel and Canny. Hough transform. Line detection. Corner detection. The structure tensor.
- Image restoration: Inverse filtering, wiener filtering.
- Non-linear filters: Homomorphic filtering, median filter, max- and min-filter, etc.
- 1) Operations on gray scale images. Linear filters in the spatial and Fourier domain.
- 2) Resampling and interpolation.
- 3) Operations on binary images. Histogram and color tables.
- 4) Automatic thresholding and simple OCR (Optical Character Recognition).
- 5) Segmentation of cells in microscopy images.
- 6) Automatic counting of blood cells.
- 7) Image restoration. Edge detection with Hough transform and Canny. Non-linear filters.
Teaching and working methods
The course consists of lectures, lessons and laboratory assignments based on Matlab.
Examination
| LAB2 | Laboratory Work | 2 credits | U, G |
| TEN1 | Written Examination | 4 credits | U, 3, 4, 5 |
Grades
Four-grade scale, LiU, U, 3, 4, 5Other information
Supplementary courses: Multidimensional signal analysis, Computer Vision, Image Sensors, Image and Audio Coding, Neural Networks and Learning Systems, Medical Image Analysis, Visual Object Recognition and Detection, Project courses regarding images.
Department
Institutionen för systemteknikDirector of Studies or equivalent
Klas NordbergExaminer
Maria MagnussonCourse website and other links
https://www.cvl.isy.liu.se/education/undergraduateEducation components
Preliminary scheduled hours: 62 hRecommended self-study hours: 98 h
Course literature
Additional literature
Books
- Gonzalez och Woods, Digital Image Processing
Websites
- Power-Pointpresentationer från föreläsningarna.
Compendia
- Laborationshäfte i digital bildbehandling.
| Code | Name | Scope | Grading scale |
|---|---|---|---|
| LAB2 | Laboratory Work | 2 credits | U, G |
| TEN1 | Written Examination | 4 credits | U, 3, 4, 5 |
Regulations (apply to LiU in its entirety)
The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.
LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva.
Additional literature
Books
Gonzalez och Woods, Digital Image Processing
Websites
Power-Pointpresentationer från föreläsningarna.
Compendia
Laborationshäfte i digital bildbehandling.
Note: The course matrix might contain more information in Swedish.
I = Introduce, U = Teach, A = Utilize
| I | U | A | Modules | Comment | ||
|---|---|---|---|---|---|---|
| 1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
| 1.1 Knowledge of underlying mathematics and science (G1X level) |
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|
X
|
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| 1.2 Fundamental engineering knowledge (G1X level) |
|
X
|
X
|
LAB2
TEN1
|
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| 1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
X
|
X
|
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LAB2
TEN1
|
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| 1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
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| 1.5 Insight into current research and development work |
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| 2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
| 2.1 Analytical reasoning and problem solving |
|
X
|
X
|
LAB2
TEN1
|
||
| 2.2 Experimentation, investigation, and knowledge discovery |
|
X
|
X
|
LAB2
|
||
| 2.3 System thinking |
|
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| 2.4 Attitudes, thought, and learning |
|
X
|
X
|
LAB2
|
||
| 2.5 Ethics, equity, and other responsibilities |
|
|
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| 3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
| 3.1 Teamwork |
|
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X
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| 3.2 Communications |
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| 3.3 Communication in foreign languages |
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X
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| 4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
| 4.1 External, societal, and environmental context |
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| 4.2 Enterprise and business context |
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| 4.3 Conceiving, system engineering and management |
X
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X
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X
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LAB2
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| 4.4 Designing |
X
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X
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X
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LAB2
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| 4.5 Implementing |
X
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X
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X
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LAB2
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| 4.6 Operating |
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| 5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
| 5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
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| 5.2 Economic conditions for knowledge development |
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| 5.3 Identification of needs, structuring and planning of research or development projects |
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| 5.4 Execution of research or development projects |
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| 5.5 Presentation and evaluation of research or development projects |
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