Shape representation and description

The last chapter was devoted to image segmentation methods which showed how to construct homogeneous regions of images and/or their boundaries. Recognition of image regions is one of the most important steps on the way to understanding image data, and requires an exact region description in a form suitable for a classifier (Chapter 7). This description step should generate a numeric feature vector, or a non-numeric syntactic description word, which characterizes properties (for example, shape) of the described region. Region description is the third of the four levels given in Chapter 3, implying that the description already comprises some abstraction — for example, 3D real objects can be represented in a 2D plane. Nevertheless, shape properties used for object description are usually computed in two dimensions. If we are interested in a 3D object description, we have to process at least two images of the same object taken from different viewpoints (stereo vision), or derive the 3D shape from a sequence of images if the object is in motion. A 2D shape representation is sufficient in the majority of practical applications, but if 3D information is necessary — if, say, a 3D object reconstruction is the processing goal, or the 3D characteristics bear the important information — the object description task is much more difficult; these topics are introduced in Chapter 9. In the following sections we will limit our discussion to 2D shape features and proceed under the assumption that described objects result from the image segmentation process.