Image Segmentation and Edge Detection What is this chapter about? This chapter is about those Image Processing techniques that are used in order to prepare an image as an input to an automatic system. These techniques perform vision image segmentation and edge detection, and their purpose is to extract information from an image in such a way that the output image contains much less information than the original one, but the little information it contains is much more relevant to the other modules of an automatic vision system than the discarded information. . | Image Processing The Fundamentals. Maria Petrou and Panagiota Bosdogianni Copyright 1999 John Wiley Sons Ltd Print ISBN 0-471-99883-4 Electronic ISBN 0-470-84190-7 Chapter 7 Image Segmentation and Edge Detection What is this chapter about This chapter is about those Image Processing techniques that are used in order to prepare an image as an input to an automatic vision system. These techniques perform image segmentation and edge detection and their purpose is to extract information from an image in such a way that the output image contains much less information than the original one but the little information it contains is much more relevant to the other modules of an automatic vision system than the discarded information. What exactly is the purpose of image segmentation and edge detection The purpose of image segmentation and edge detection is to extract the outlines of different regions in the image . to divide the image in to regions which are made up of pixels which have something in common. For example they may have similar brightness or colour which may indicate that they belong to the same object or facet of an object. How can we divide an image into uniform regions One of the simplest methods is that of histogramming and thresholding If we plot the number of pixels which have a specific grey level value versus that value we create the histogram of the image. Properly normalized the histogram is essentially the probability density function for a certain grey level value to occur. Suppose that we have images consisting of bright objects on a dark background and suppose that we want to extract the objects. For such an image the histogram will have two peaks and a valley between them. We can choose as the threshold then the grey level value which corresponds to the valley of the histogram indicated by to in Figure and label all pixels with grey 266 Image Processing The Fundamentals Figure The histogram of an image with a bright object on a dark .
