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CS413 Image and Video Analysis (not running 2018/19)

CS413 15 CATS (7.5 ECTS) Term 1


Option - MEng CS and DM, MSc CS, MSc DA


CS118, CS131 or equivalent knowledge and experience

Academic Aims

The module aims to teach the fundamentals of digital image processing, image and video analysis. In particular, it will present the mathematics and algorithms that underlie image analysis techniques such as filtering, denoising, edge detection, feature detection, tracking and 3D reconstruction. It will also present how these tools are used in algorithms image and video segmentation, motion estimation, stereo reconstruction, video denoising and video analysis, object detection and recognition, and standards for video compression and communication.

The course will enable computer science undergraduates to apply their mathematical knowledge and understanding of algorithms to problems in image and video processing: from preprocessing, to quantitation, video compression and video interpretation. The methods have numerous applications e.g. in medicine, biology, robotics (computer vision), surveillance, security, biometrics, database searching, TV and entertainment.

Learning Outcomes

At the end of the module the student should be able to:

  • Understand and describe the fundamental principles of image and video analysis and have an idea of their application.
  • Perform written communication in lab report and examination. Use specialist toolboxes in Matlab programming. Critically analyse multimedia applications
  • Assimilate mathematical and algorithmic material on image processing fundamentals and appropriately apply and program solutions to real problems in image and video analysis.


  • Introduction to Human visual perception
  • Image sampling and quantization.
  • Filtering by convolution and correlation: blurring, sharpening, edge detection.
  • Colour models and contrast processing.
  • Histograms and basic statistical models of images.
  • Information and entropy.
  • Discrete Fourier transforms and Discrete Cosine Transforms for filtering, coding, correlation and motion estimation.
  • Image Pyramids for analysis and image compression.
  • Introduction to Wavelet transforms.
  • Image and video denoising.
  • Image and video segmentation and texture models.
  • Algorithms for boundary and region segmentation from images and video.
  • Optical flow techniques for motion estimation.
  • Pinhole camera models and stereo and 3D reconstruction
  • Object detection in videos.
  • Algorithms and standards for video compression and video coding.
  • Region and feature point tracking.


  • R.C. Gonzalez and R.E. Woods. Digital Image Processing. 3rd Edition. Addison Wesley, 2007.
  • R.C. Gonzalez and R.E. Woods and S. L. Eddins. Digital Image Processing using MATLAB(R). Adisson Wesley 2003.
  • Mark S. Nixon and Alberto S. Aguado. Feature Extraction and Image Processing for Computer Vision. Academic Press. 2012
  • M. Ghanbari. Standard Codecs: Image compression to Advanced Video Coding. IET. 3rd Edition, 2011.
  • Simon J. D. Prince. Computer Vision: Models, Learning and Inference. Cambridge University Press, 2013.


Two hour examination (70%), laboratories and coursework (30%)


30 one-hour lectures plus 8 two-hour workshops