- Fundamental programming and algorithms
- Fundamental image processing
- Fundamental video processing
Expected learning outcomes
Expected learning outcomes
Having completed the course, the student should have gained knowledge, skills and general competences related to selected topics in video processing.
- 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.
- 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.
- 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.
- 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
Net Support Learning
Form(s) of Assessment
Form(s) of Assessment (additional text)
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.
- A term paper and a final paper.
- Candidates must pass all parts.
External examiner (or external and internal) within 5 years period, next time at latest in 2017.
The whole course must be repeated.
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.