Image processing and analysis
2012-2013 - IMT4811 - 5 ECTS

Expected learning outcomes

On completion of this course the student will be able to:


  • Posses an understanding of the fundamental characteristics of digital systems used in imaging, together with general concepts of science, quantitative methods.
  • Possess advanced knowledge of basic algorithms for image manipulation, characterization, filtering, segmentation, feature extraction and template matching in direct space and Fourier space.
  • Posses advanced knowledge of (i.e. to describe, analyse and reason about) 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 specialized insight and good understanding of the research frontier in a selected part of the media technology area.


  • 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 colour imaging.
  • Are able to to explore a range of practical techniques, by developing their own simple processing functions using library facilities and tools such as Matlab.
  • 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.  Digital image acquisition: analogue to digital conversion. Sampling and quantization. Look-up table conversions. Scaling.
  2.  Digital image formats: representation and description. Image encoding and image compression.
  3.  Image filtering: linear and non-linear filtering operations. Image convolution. Separable convolutions. Image enhancement. Image restoration.
  4.  Digital image processing: histogram manipulation. Thresholding. Image segmentation. Clustering techniques. Split and merge algorithms. Region processing. Edge detections. Region adjacency graph.
  5.  Image transformations: histogram equalization, geometric transformations, affine transformations, polynomial warps.
  6.  Digital image analysis: noise analysis. Texture analysis. Fourier descriptors. Features extraction. Pattern recognition. Corner detection. Saliency maps. Image interpretation. Motion analysis.
  7.  Color image analysis : representation, encoding, scalar and vector approaches. Clustering techniques. Color invariants. Color constancy algorithms.
  8.  Template matching: Similarity and dissimilarity metrics. Cross-correlation. Multiresoultion algorithms. Graph matching. Image retrieval. 2D object detection, recognition and Positioning.
  9.  High level image descriptors. Semantic image description MPEG7.

Teaching Methods

Laboratory work
Net Support Learning

Teaching Methods (additional text)

Lectures by the course teacher and guest lecturers.

Lab sessions and homework assignments. Two or three of the homework assignments will be graded.

E-learning material: lectures notes in PDF and audio recordings of the lectures and important exercises are available on the Fronter system. Additionally communication between the teachers and the students, and among the students, will be facilitated by the Fronter.

Form(s) of Assessment

Written exam, 3 hours

Form(s) of Assessment (additional text)

  • Written exam (60%)
  • Homework assignments (two or three of the homework assignments will be graded.) (40%)
  • Each part must be individually approved of.

Grading Scale

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

External/internal examiner

Internal examiner for the written exam and homework assignments.

Re-sit examination

Written exam: ordinary re-sit examination

Examination support

English translation dictionary.

Teaching Materials

Course book:

  •  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

Partial overlap with IMT4401 Digital Image Reproduction