Image processing and analysis
2015-2016 - IMT4202 - 10 ECTS

On the basis of

The following topics must be mastered from the bachelor level:

  • Fundamental programming
  • Fundamental calculus including trigonometric functions, logarithms and the exponential function
  • Fundamental linear algebra
  • Fundamental complex calculus including the complex exponential function
  • Arithmetic and geometric series

Expected learning outcomes

On completion of this course the students:

  • Possess an understanding of the fundamental characteristics of digital systems used in imaging, together with general concepts of science, quantitative methods.
  • Possess advanced knowledge of (i.e. to describe, analyse and reason about) basic algorithms for image manipulation, characterization, filtering, segmentation, feature extraction and template matching in direct space and Fourier space.
  • Possess advanced knowledge of how monochrome digital images are represented, manipulated, encoded and processed, with emphasis on algorithm design, implementation and performance evaluation.
  • Possess advanced knowledge of methods of capturing and reproducing images in digital systems.
  • Possess knowledge and understanding of the mathematical methods commonly used for representing, compressing and processing signals and images.


  • Are able to use mathematical techniques in colour imaging and demonstrate the use of tools such as spreadsheets and specialist maths applications to solve problems in signal and image processing.
  • Are able to to explore a range of practical techniques, by developing their own simple processing functions using library facilities and tools such as, e.g., Matlab or Python.
  • Are able to implement the techniques in the topics studied and compare their performances in certain image processing tasks.
  • Are able to use relevant and suitable methods when carrying out research and development activities in the area of image processing

General competence

  • Have the learning skills to continue acquiring new knowledge and skills in a manner that is largely self-directed
  • Are able to contribute to innovative thinking and innovation processes


  1. Vector spaces, signals, and images
  2. Digital image acquisition: analogue to digital conversion, sampling and quantization,  look-up table conversions, scaling.
  3. Digital image formats: representation and description.
  4. The discrete Fourier transform (DFT)
  5. The discrete cosine transform (DCT)
  6. Digital image processing: histogram manipulation, thresholding, image segmentation, clustering techniques, split and merge algorithms, region processing, edge detection, region adjacency graph.
  7. Image transformations and histogram equalization
  8. Convolution and filtering: linear and non-linear filtering operations, mage convolution, separable convolutions, image enhancement, image restoration.
  9. Filter banks
  10. Wavelets and the discrete wavelet transform (DWT)
  11. Digital image analysis: noise analysis, texture analysis, fourier descriptors, feature extraction, pattern recognition, corner detection, saliency maps.
  12. Color image analysis: representation, encoding, scalar and vector approaches, clustering techniques, color invariants, color constancy algorithms.
  13. Template matching: similarity and dissimilarity metrics, cross-correlation,  multi-resolution algorithms, graph matching, image retrieval., 2D object detection, recognition and positioning.
  14. High level image descriptors.

Teaching Methods

Laboratory work
Net Support Learning

Teaching Methods (additional text)

The course will be offered both as an ordinary campus course and in a flexible way to off-campus students. Lecture notes, e-lectures and other types of e-learning material will be offered through Fronter. Communication between the teachers and the students, and among the students, will be facilitated by Fronter.

Form(s) of Assessment

Portfolio Assessment
Oral exam, individually

Form(s) of Assessment (additional text)

  • Portfolio (counts 40%)
  • Oral, individual exam (counts 60%)
  • Both parts must be passed

The portfolio consists of up to 10 assignments and one project and is handed in individually. There is continuous assessment of each of the assignments before the final submission date of the portfolio. The portfolio will be given one single grade.

Grading Scale

Alphabetical Scale, A(best) – F (fail)

External/internal examiner

Two internal examiners.

Re-sit examination

Re-sit examination of oral exam by appointment with the course responsible. No re-sit examination of the portfolio.

Examination support


Coursework Requirements


Teaching Materials

Course books:

  • Discrete Fourier Analysis and Wavelets - Applications to Signal and Image Processing, by Broughton, S. Allen and Kurt Bryan (2008). New Jersey: John Wiley & Sons, Inc.
  • Digital Image Processing, 3rd Edition (DIP/3e), by Rafael C. Gonzalez and Richard E. Woods, Prentice Hall (2008)

Further reading material:

  • Digital Image Processing Using MATLAB (DIPUM), by Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins, Prentice Hall (2004).
  • Color Image Processing: Methods and Applications (Image Processing), by Rastislav Lukac & Kostantinos N. Plataniotis, CRC (2006)
  • The Image Processing Handbook, Fifth Edition (Image Processing Handbook), by John C. Russ, CRC (2006)

Replacement course for

IMT4811 and IMT4991

Additional information

Credit reduction due to overlapping courses: 5 ECTS with IMT4811 and 5 ECTS with IMT4991