CSA3020

Lecture 5 - Images and Graphics

Reference: Steinmetz, R., and Nahrstedt, K. (1995). Multimedia: Computing, Communications & Applications. Prentice Hall. Chapter 4.
Steinmetz, R. and Nahrstedt, K. (2002). Multimedia Fundamentals: Volume 1. Prentice Hall. Chapter 4.

Introduction

Most multimedia is presented in a 2-dimensional medium, the computer monitor. In future, immersive environments (e.g., virtual reality) will present information in a 3-dimensional environment. We, however, will concentrate on 2D and 3D digital images which are presented in a predominantly 2D medium.

Digitizing Images

As with sound, an image which exists outside of a computer environment needs to be digitized prior to being manipulated (although graphics can be created in digital form using graphics packages or computer-based drawing tools). There are an infinite number of natural colours (including their shades), but, as usual, only a finite number of them can be represented within a computer environment. The number of colours supported by a computer monitor (through which, after all, the graphic will primarily be viewed) is known as the colour-depth, and can be 1, 2, 4, 8, 16, or 24-bits deep (representing monochrome, 4, 16, 256, thousands and millions of colours or grey-scale respectively). Some high-end monitors can support a colour-depth greater than 24-bits, but usually the extra bits are used for special effects, rather than representing an increased range of colours.
A digital image is represented by a matrix of numeric values, each representing a quantized intensity value. The points at which an image is sampled are called pixels. The resolution is the number of samples taken of the image. Within each pixel, the intensity is averaged out to give a single value. Obviously, the higher the resolution, the greater the number of samples, the smaller the area of each pixel, and, consequently, the better the definition of the digital image.
Compare Figures 1 and 2 below. The image in figure 2 will resolve to a better (although still fuzzy) representation of the original than the image in Figure 1.

Figure 1. A 2x2 grid superimposed over the original image.

Figure 2. The same image with a 52x43 grid.

Essentially, the intensity in each pixel will be averaged through digitization. Although both resulting digitized images will be of poor quality, the 2x2 image will be completely undiscernible, whereas the 52x43 image will show the basic shape of the original image.
An image may be captured in monochrome (1-bit), grey-scale (usually 8-bit), or colour (usually 24-bit). In each case, the intensity of each sample taken is converted to its closest match. In the case of monochrome images, the intensity of each pixel is thresholded at 50%. If the average intensity of the sample is closer to black than it is to white, then it is store as binary 1, otherwise it is stored as binary 0. In the case of grey-scale there are (usually) 256 levels of grey. Again, the quantized value is stored as the closest approximation to the appropriate level of grey. However, in the case of colour images, a different technique is needed. All colours can be represented by selective mixing of the three primary colours red, green, and blue. There are other ways of representing colour, but RGB is by far the most common.

Stored Image Format

A digital image is stored as a 2-dimensional array of values, where each value represents the data associated with a pixel in the image. In the case of bitmaps, the value is 0 or 1, which represent monochrome images. In the case of a colour image, the value can be:

The storage space required for an image is the resolution of the image multiplied by the colour depth. For example, a 640x480 resolution image in millions of colours requires 640x480x24 = 7,372,800 bits, or 900K. Smaller space requirements can be obtained by compressing the image.

Computer-generated graphics

Graphics can also be created from scratch using a graphics editor, e.g. xphigs. In this case, a graphic is specified through graphics primitives and their attributes, rather than by a pixel matrix. This gives the advantage that components of the image can by manipulated through the primitives (e.g., line, square, ellipse), whereas with a digitized image it is only possible to manipulate the image at the pixel level.
These graphics occupy less space than a corresponding digitized image of the same resolution and colour-depth. However, before the graphic can be rendered on the screen it needs to be converted into a pixel matrix. Some graphics packages also allow objects to be labeled (e.g., if you draw a chair you can label that object as a chair). This is of particular interest to content-based image retrieval.

Computer Image Processing

Image processing has two main areas: image synthesis and image analysis. Image synthesis, has already been covered to an introductory level in CSA2120, and will not be discussed further in this lecture series. Image analysis is concerned with recovering from graphics information which is necessary for scene analysis, e.g., automatically discerning what is in a scene, and being able to reason about topographical relationships between objects in a scene. Some application areas are object identification and tracking (tracking, obviously, in an environment where there are a sequence of images such as video), image enhancement (to improve the quality of a digitized image), pattern detection and recognition (e.g., optical character recognition), and scene analysis and computer vision (e.g., visual planning systems, and reconstructing 3D scenes from stereoscopic images). Of obvious importance is the ability to accurately reconstruct 3D environments for virtual realities.

Image Recognition

Figure 5.3 shows the variety of steps required to transform iconic information into recognition information.

Figure 5.3. Image Recognition Steps. (From Steinmetz, R., and Nahrstedt, K., 1995, pg. 72/Steinmetz, R. and Nahrstedt, K., 2002 , pg. 69).

Image recognition is usually performed on digital images which are represented by a pixel matrix. The only information available to an image recognition system is the light intensities of each pixel and the location of a pixel in relation to its neighbours. From this information, image recognition systems must recover information which enables objects to be located and recognised, and, in the case of stereoscopic images, depth information which informs us of the spatial relationship between objects in a scene.

Image Formatting
Image Formatting means capturing an image by bringing it into a digital form -- already covered in the section on digitizing images.

Conditioning
In an image, there are usually features which are uninteresting, either because they were introduced into the image during the digitization process as noise, or because they form part of a background. An observed image is composed of informative patterns modified by uninteresting random variations. Conditioning suppresses, or normalizes, the uninteresting variations in the image, effectively highlighting the interesting parts of the image.
Labeling
Informative patterns in an image have structure. Patterns are usually composed of adjacent pixels which share some property such that it can be inferred that they are part of the same structure (e.g., an edge). Edge detection techniques focus on identifying continuous adjacent pixels which differ greatly in intensity or colour, because these are likely to mark boundaries, between objects, or an object and the background, and hence form an edge. After the edge detection process is complete, many edge will have been identified. However, not all of the edges are significant. Thesholding filters out insignificant edges. The remaining edges are labeled. More complex labeling operations may involve identifying and labeling shape primitives and corner finding.
Grouping
Labeling finds primitive objects, such as edges. Grouping can turn edges into lines by determining that different edges belong to the same spatial event. The first 3 operations represent the image as a digital image data structure (pixel information), however, from the grouping operation the data structure needs also to record the spatial events to which each pixel belongs. This information is stored in a logical data structure.
Extracting
Grouping only records the spatial event(s) to which pixels belong. Feature extraction involves generating a list of properties for each set of pixels in a spatial event. These may include a set's centroid, area, orientation, spatial moments, grey tone moments, spatial-grey tone moments, circumscribing circle, inscribing circle, etc. Additionally properties depend on whether the group is considered a region or an arc. If it is a region, then the number of holes might be useful. In the case of an arc, the average curvature of the arc might be useful to know. Feature extraction can also describe the topographical relationships between different groups. Do they touch? Does one occlude another? Where are they in relation to each other? etc.
Matching
Finally, once the pixels in the image have been grouped into objects and the relationship between the different objects has been determined, the final step is to recognise the objects in the image. Matching involves comparing each object in the image with previously stored models and determining the best match template matching.

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Date last amended: 2nd September 2002