TABLE OF CONTENTS An analysis of the spatial relations of the gray values in a small neighborhood provides the first clue for the recognition of objects in images. Let us take a scene containing objects with uniform radiance as a simple example. If the gray value does not change in a small neighborhood, the neighborhood lies within an object. If, however, the gray value changes significantly, an edge of an object crosses the neighborhood. In this way, we recognize areas of constant gray values and edges.
Just processing individual pixels in an image by point operations does not provide this type of information. In Chapter 10 we show in detail that such operations are only useful as an initial step of image processing to correct inhomogeneous and nonlinear responses of the imaging sensor, to interactively manipulate images for inspection, or to improve the visual appearance.
A new class of operations is necessary that combines the pixels of a small neighborhood in an appropriate manner and yields a result that forms a new image. Operations of this kind belong to the general class of neighborhood operations. These are the central tools for low-level image processing. This is why we discuss the possible classes of neighborhood operations and their properties in this chapter.
The result of any neighborhood operation is still an image. However, its content has been changed. A properly designed neighborhood operation to detect edges, for instance, should...
