Image Processing

Image processing involves changing the nature of an image in order to either
1.improve its pictorial information for human interpretation.
2.Render it more suitable for autonomous machine perception.
It is convenient to subdivide different image processing algorithms into broad subclasses.There are different algorithms for different tasks and problems, and often we would like to distinguish the nature of the task at hand.
Acquiring the image.First we need to produce a digital image from a paper envelope. This can be done using either a CCD camera or a scanner.
Pre-processing.This is the step taken before the _major_ image processing task. The problem here is to perform some basic tasks in order to render the resulting image more suitable for the job to follow. In this case, it may involve enhancing the contrast, removing noise, or identifying regions likely to contain the postcode.
Segmentation.Here is where we actually _get_ the postcode; in other words, we extract from the image that part of it which contains just the postcode.
Representation and description.These terms refer to extracting the particular features which allow us to differentiate between objects. Here we will be looking for curves, holes and corners which allow us to distinguish the different digits which constitute a postcode.
Recognition and interpretation.This means assigning labels to objects based on their descriptors (from the previous step), and assigning meanings to those labels. So we identify particular digits, and we interpret a string of four digits at the end of the address as the postcode.
It is necessary to realize that these two aspects represent two separate but equally important aspects of image processing. A procedure which satisfies a condition:-
1. A procedure which makes an image “look better” may be the very worst procedure for the satisfying condition
Examples may include:

• Enhancing the edges of an image to make it appear sharper; Sharpening edges is a vital component of printing: in order for an image to appear “at its best” on the printed page; some sharpening is usually performed.
• Removing “noise” from an image; noise being random errors in the image. Noise is a very common problem in data transmission: all sorts of electronic components may affect data passing through them, and the results may be undesirable.
• Removing motion blur from an image. Motion blur may occur when the shutter speed of the camera is too long for the speed of the object. In photographs of fast moving objects: athletes, vehicles, for example, the problem of blur may be considerable.

2. Humans like their images to be sharp, clear and detailed. Machines prefer their images to
be simple and uncluttered.
Examples may include:

• Obtaining the edges of an image. This may be necessary for the measurement of objects in an image, once we have the edges we can measure their spread and the area contained within them. We can also use edge detection algorithms as the first step in edge enhancement, as we saw above.
• Removing detail from an image. For measurement or counting purposes, we may not be interested in all the detail in an image. For example, a machine inspected items on an assembly line, the only matters of interest may be shape, size or colour. For such cases, we might want to simplify the image.

Some Applications
Image processing has an enormous range of applications; almost every area of science and technology can make use of image processing methods. Here is a short list just to give some indication of the range of image processing applications.
1. Medicine:Inspection and interpretation of images obtained from X-rays, MRI or CAT
scans, analysis of cell images, of chromosome karyotypes.
2. Agriculture:Satellite/aerial views of land, for example, to determine how much land is being used for different purposes, or to investigate the suitability of different regions for different crops, inspection of fruit and vegetables distinguishing good and fresh produce from old.
3. Industry:Automatic inspection of items on a production line, inspection of paper samples.
4. Law enforcement:Fingerprint analysis, sharpening or de-blurring of speed-camera images.