17-12-2012, 05:59 PM
Digital Image Processing Using MATLAB
1Digital Image.pdf (Size: 1.67 MB / Downloads: 74)
Introduction
As mentioned in the previous chapter, the power that MATLAB brings to digital image processing is an extensive set of functions for processing multidimensional arrays of which images (two-dimensional numerical arrays) are a special case. The Image Processing Toolbox is a collection of functions that extend the capability of the MATLAB numeric computing environment. These functions, and the expressiveness of the MATLAB language, make image-processing operations easy to write in a compact, clear manner, thus providing an ideal software prototyping environment for the solution of
image processing problems. In this chapter we introduce the basics of MATLAB
notation, discuss a number of fundamental toolbox properties and functions, and begin a discussion of programming concepts. Thus, the material in this chapter is the foundation for most of the software-related discussions in the
remainder of the book.
Digital Image Representation
An image may be defined as a two-dimensional function fxy(,), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates is called the intensity of the image at that point. The term gray level is used
often to refer to the intensity of monochrome images. Color images are formed by a combination of individual images. For example, in the RGB color system a color image consists of three individual monochrome images, referred to as the red ®, green (G), and blue (B) primary (or component) images. For this reason, many of the techniques developed for monochrome images can be extended to color images by processing the three component images individually. Color image processing is the topic of Chapter 7.
Coordinate Conventions
The result of sampling and quantization is a matrix of real numbers. We use two principal ways in this book to represent digital images. Assume that an image fxy(,) is sampled so that the resulting image has M rows and N columns. We say that the image is of size MN*. The values of the coordinates are discrete quantities. For notational clarity and convenience, we use integer values for these discrete coordinates. In many image processing books, the image origin is defined to be at (,)(,)xy=00. The next coordinate values along the first row of the image are (,)(,)xy=01. The notation (,)01 is used to signify the second sample along the first row. It does not mean that these are the actual values of physical coordinates when the image was sampled. Figure 2.1(a) shows this coordinate convention. Note that x ranges from 0 to M-1 and y from 0 to N-1 in integer increments.