Page 8 - DCAP313_LAB_ON_COMPUTER_GRAPHICS

Lab on Computer Graphics

Notes Image Representation
How digital images are characterized in a computer. This ‘mini’-topic explores different forms
of frame-buffer for storing images, and also different ways of representing colour and key issues
that arise in colour.

Geometric Transformation
How to use linear algebra, e.g. matrix transformations, to manipulate points in space. This work
focuses heavily on the concept of reference frames and their central role in Computer Graphics.
Also on this theme, Eigen value decomposition is discussed and a number of applications relating
to visual computing are explored.
OpenGL Programming
Discusses how the mathematics on this course can be implemented directly in the C programming
language using the OpenGL library. Note much of this content is covered in PowerPoint handouts
sooner than these notes.

Geometric Modeling
Whereas center on manipulating/positioning of points in 3D space, how these points can be
“joined up” to form curves and surfaces. This allows the modelling of objects and their trajectories.
The computer graphics is a huge field that encompasses almost any graphical facet; we are
mainly interested in the generation of images of 3-dimensional scenes. Computer imagery has
applications for film special effects, simulation and training, games, medical imagery, flying
logos; etc. Graphics relies on an internal model of the view, that is, a mathematical representation
suitable for graphical computations. The model describes the 3D shapes, layout and of the scene.
This 3D representation then has to be projected to compute a 2D image from a given viewpoint.
the involves projecting the objects (perspective), handling (which parts of objects are hidden)
and computing their appearance and lighting interactions, for animated sequence, the motion
of objects has to be specified.
Pixel

A computer image is usually represented as an isolated grid of picture elements a.k.a. pixels.
The number pixel determines the resolution of the image (See Figure 1.1). Typical resolutions
range from 320*200 to 2000*1500. For a black and white image, a number explains the
intensity of each pixel. It can be expressed between 0.0 (black) and 1.0 (white). However, for
internal binary representation reasons, it is usually stored as an integer between 0 (black) and
255 (white).

Figure 1.1: The Low Resolution Digital Image.
Left: Black and White. Right: Colour

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