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CS413 Image and Video Analysis

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.
  • Algorithms for boundary and region segmentation from images and video.
  • Background subtraction.
  • Pinhole camera models and stereo and 3D reconstruction
  • Object detection in videos.
  • Face recognition.

15 CATS (7.5 ECTS)
Term 1

Abhir Bhalerao


Online material