TABLE OF CONTENTS All image processing operations discussed in the preceding chapters aimed at a better recognition of objects of interest, i. e., at finding suitable local features that allow us to distinguish them from other objects and from the background. The next step is to check each individual pixel to see whether it belongs to an object of interest or not. This operation is called segmentation and produces a binary image. A pixel has the value one if it belongs to the object; otherwise it is zero. Segmentation is the operation at the threshold between low-level image processing and image analysis. After segmentation, we know which pixel belongs to which object. The image is parted into regions and we know the discontinuities as the boundaries between the regions. After segmentation, we can also analyze the shape of objects with operations such as those discussed in Chapter 19.
In this chapter, we discuss several types of elementary segmentation methods. Basically we can think of several basic concepts for segmentation. Pixel-based methods (Section 16.2) only use the gray values of the individual pixels. Region-based methods (Section 16.4) analyze the gray values in larger areas. Finally, edge-based methods (Section 16.3) detect edges and then try to follow them. The common limitation of all these approaches is that they are based only on local information. Even then...
