COMP27112: Computer Graphics and Image Processing (2010-2011)
Visual Computing brings together two fundamentally important aspects of modern computing: Computer Graphics ? concerned with the synthesis of images from computer models ? and Image Processing, which deals with analysis and understanding of images by computers. There are now considerable overlaps between these two, traditionally separate, fields of research and their applications.
The Visual Computing theme consists of the following course units:
Year 2: Computer Graphics and Image Processing (10 credits)
Year 3: Advanced Computer Graphics (10 credits)
Year 3: Computer Vision (10 credits)
The importance of visual interfaces has never been greater. Graphical interfaces have become ubiquitous, from desk-top interaction, to games and three-dimensional virtual environments. In parallel, there has been an explosion in digital image processing and analysis. We take for granted digital photography and video, while our health services rely on digital X-ray systems, CT and MRI scanners for seeing inside our bodies. Meanwhile, the visualization of computer simulations is an essential aspect of product design and testing, genome exploration, drug design, and climate modelling. The demand for computer scientists with advanced knowledge of such areas has never been greater.
The theme will enhance your knowledge and understanding, answering such questions as:
How are three-dimensional environments represented in a computer, and how are interactive 3D worlds created?
How are 2D and 3D representations combined ? for example, how can we recover 3D geometry from 2D images?
How are the basic mathematical techniques and algorithms used to build useful applications?
How are images stored, processed and manipulated?
How can computers interpret images captured by cameras and other recording devices?
|Programme outcome||Unit learning outcomes||Assessment|
|A1 D5||Have knowledge and understanding of image processing algorithms and applications.|
|A1 D5||Have knowledge and understanding of image transforms, including image representations, resolution, point, local and global transforms.|
|A1 D5||Have knowledge and understanding of image enhancement including image smoothing and sharpening, image segmentation, image morphology.|
|A1 D5||Have a knowledge and understanding of the structure of an interactive computer graphics system, and the separation of system components.|
|A1 D5||Have a knowledge and understanding of geometrical transformations and 3D viewing.|
|A1 D5||Be able to create interactive graphics applications using OpenGL.|
|A1 D5||Have a knowledge and understanding of techniques for representing 3D geometrical objects.|
|A1 D5||Have a knowledge and understanding of the fundamental principles of local and global illumination models.|
Fundamentals (1 week)
2 and 3 D Coordinate systems. Vectors, matrices and basic vector/matrix operations.2 and 3 D geometric transformations (translation, rotation, scaling, affine).
3D Modelling and Illumination (5 weeks)
The camera model. Viewing and projection. Points, lines, B?zier curves. Polygons. Local illumination: ambient, diffuse and specular components. Interpolation: intensity (Gouraud) and normal vector (Phong). Surface detail: textures, bump mapping. Model structuring using scene graphs. Masters and instances. Inheritance of transformations and attributes.
OpenGL (in C) laboratory exercises. Self-paced Coursework Assignments using example programs and software tools (with a small amount of experiment-driven programming).
Image Transformations (2 weeks)
Pixels, pixel values, grey level, spatial resolution, colour representations, image transformations: point transformations (windowing, histogram equalisation, colour transformations ? colour spaces).
MATLAB programming, MATLAB exercises in image manipulation. Manipulating greyscale images. Matrix manipulation in MATLAB, image translation, rotation and scaling (bilinear interpolation), affine transformations.
Image Enhancement (3 weeks)
Local processes, convolution, image smoothing (local averaging, weighted averaging), size of support, Gaussian mask. Edge enhancement (unsharp masking). Edge detection (Prewitt, Sobel, Canny), Thresholding, blob detection, simple measurement (geometric features). Rank order filters (median, max-min).
MATLAB exercises in image processing. Noise reduction. General image convolution code. Using the code to make an edge detector (Sobel, Prewitt), Combined smoothing and edge detection ? scale.