MSc in Electrical Engineering

Course Information Package

Course Unit CodeAEEE573
Course Unit DetailsPhD Computer Science (Postgraduate level courses) - MSc Electrical Engineering (Technical Electives) - PhD Electrical Engineering (Postgraduate level courses) -
Number of ECTS credits allocated7
Learning Outcomes of the course unitBy the end of the course, the students should be able to:
  1. Employ techniques for image enhancement, restoration, coding and compression
  2. Use image frequencies and apply transformations, such as the Fourier transform on images
  3. Implement and apply several image filtering techniques
  4. Implement image compression algorithms
  5. Analyse images using analysis techniques like quality assessment and template matching
  6. Employ video capturing, analysis and improvement methods
  7. Apply Video motion estimation
  8. Write programs in Matlab or a high level language to implement image processing algorithms
Mode of DeliveryFace-to-face
Recommended optional program componentsNONE
Course Contents

Introduction to Digital Image Processing: Define and understand the several types of images. Understand concepts of Imaging geometry, Imaging Devices, Image acquisition and Image Representation

Image Histogram and Point Operations: Understand what the histogram of an Image represents. How can we apply Linear Point operations, Nonlinear point operations, Histogram Shaping and Matching, Algebraic Image Operations, Geometric Image Operations

Discrete Fourier Transform: Sinusoidal Image, Discrete Fourier Transform, Meaning of Image Frequencies, Sampling Theorem

Linear Gray Scale Image Filtering: Understand concepts of Linear Gray Scale Image filtering, linear image denoising linear image restoration

Non Linear Gray Scale Image Filtering: Understand concepts of Non-Linear Gray Scale Image filtering and apply filters like median. Understand image noise and modelling.

Image Compression: Understand binary Image and their creation. Logical Operations on images. Apply algorithms for Blob Coloring, Binary Morphology, Binary Image Compression

Image and Video Analysis: Image quality assessment, noise models, image and video segmentation

Video Acquisition and Analysis: Video acquisition techniques,  motion estimation using general methodologies or specific like block matching algorithms or meshed based motion estimation

Laboratory Work: Read gray scale images, present histogram, find the optimum threshold to transform into binary, Transform gray scale to binary, count blobs, present blobs of images, Binary functions on images, OR, NOT, AND, XOR. Apply morphological filters on images, Use morphological filters on binary images, so as to change the shape. Find the average optical density of a gray level image, apply histogram shifting and scaling. Gray level images, contrast stretch and flattening. Gray level images, histogram fitting, image filtering. Fourier transform, application on images and results verification


Recommended and/or required reading:
  • The Handbook of Image and Video Processing, Al Bovik, Academic Press, 2000.
  • Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, 2nd Edition, Addison Wesley Pub. Co, 2002.
  • Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing using matlab, 2nd Edition, Addison Wesley Pub. Co, 2002.
Planned learning activities and teaching methods

Students are taught the course through lectures (4 hours per week) by means of PowerPoint presentation slides. Guided individual and/or group project and laboratory assignments are assigned to develop practical engineering skills while integrating the course theory. Further literature search is encouraged by assigning students to identify a specific problem related to some possible open research issues, gather relevant scientific information about how others have addressed the problem and report this information in written and/or orally. Lecture notes and presentations are available through the web for students to use in combination with the textbooks.

Practical sessions are held in computer laboratories where Matlab environment is being used and programming exercises are given to gain practical skills and to implement the theoretical concepts taught.

Assessment methods and criteria
Laboratory Work/homework20%
Final Exam60%
Language of instructionEnglish
Work placement(s)NO