Course Information Package
|Course Unit Title||IMAGE AND VIDEO PROCESSING|
|Course Unit Code||AEEE573|
|Course Unit Details||PhD Computer Science (Postgraduate level courses) - MSc Electrical Engineering (Technical Electives) - PhD Electrical Engineering (Postgraduate level courses) -|
|Number of ECTS credits allocated||7|
|Learning Outcomes of the course unit||By the end of the course, the students should be able to:|
|Mode of Delivery||Face-to-face|
|Recommended optional program components||NONE|
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:|
|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|
|Language of instruction||English|