IMT6161 - Selected topics in Video Processing


Examination arrangement

Examination arrangement: Term paper and final paper
Grade: Passed/Failed

Evaluation Weighting Duration Grade deviation Examination aids
Term paper 1/2
Final report 1/2

Course content

Theory and applications of motion estimation Video Coding Video Segmentation Object detection and tracking in video Multi-view video processing 3D video Video Enhancement Video quality evaluation Content-based Video Retrieval

Learning outcome

Expected learning outcomes Having completed the course, the student should have gained knowledge, skills and general competences related to selected topics in video processing.

Knowledge: The student is in the forefront of knowledge of core issues from different sub-areas of video processing research including video segmentation, video coding, video analyses, multiview video, 3D video, video enhancement and video quality evaluation, He would have achieved in-depth knowledge of one of these core areas through independent study, He would have the ability to discuss (i.e. to describe, analyze, reason about and implement) how digital video may be represented, processed, encoded and transmitted.

Skills: Make appropriate use of mathematical techniques in video processing and analyses.  Demonstrate the use of by implementing techniques such as adaptive algorithms, scalable approaches and real-time techniques to solve problems in video processing applications. '

General competences: Be able to review scientific publications from interdisciplinary areas related to video analyses, 3D and multi-view video, target tracking, activity recognition, and propose new approaches to analyze the video data. The candidate has the ability of appreciation of the impact of (i.e. to describe, analyze, reason about) recently published research in video processing.

Learning methods and activities

-Lectures -E-learning -Project work -Meeting(s)/Seminar(s)

Compulsory requirements: None

Further on evaluation

Re-sit: The whole course must be repeated.

Forms of assessment: Candidates must provide two papers. One is a term paper on a topic chosen by the candidate in coordination with the lecturer (see below), the other is a final report with an other area, beyond that covered by the candidate in the term paper, must be described concisely. Presentation on the selected topic followed by a question and answer session with some practical demonstration of the techniques when possible. Candidates must pass all parts.

Specific conditions

Required previous knowledge

  • Fundamental programming and algorithms
  • Fundamental image processing
  • Fundamental video processing

Course materials

Textbooks, monographs, and recent research articles including but not limited to:

  • Gonzalez and Woods: Digital Image Processing, Prentice Hall, 2002.
  • Aghajan and Cavallaro: Multi-camera Networks, Academic Press, 2009.
  • Ristic, Arulampalam and Gordon: Beyond Kalman Filter, Artech House, 2004.
  • MPEG standards reference documents. Recent journal and conference papers.

More on the course



Version: 1
Credits:  5.0 SP
Study level: Doctoral degree level


Term no.: 1
Teaching semester:  AUTUMN 2021

Term no.: 1
Teaching semester:  SPRING 2022

Language of instruction: English

Location: Gjøvik

Subject area(s)
  • Informatics
Contact information
Course coordinator:

Department with academic responsibility
Department of Computer Science


Examination arrangement: Term paper and final paper

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Term paper 1/2





Room Building Number of candidates
Autumn ORD Final report 1/2





Room Building Number of candidates
Spring ORD Term paper 1/2
Room Building Number of candidates
Spring ORD Final report 1/2
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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