ABSTRACT
Recognition and classification are complementary functions that lie at the “high end” (i.e., require the most complicated algorithms) in the field of image analysis, although they have been of significant interest since the beginnings (Duda & Hart, 1973). Classification is concerned with establishing criteria that can be used to identify or distinguish different populations of objects that may appear in images. These criteria can vary widely in form and sophistication, ranging from example images of presumably prototypical representatives of each class, to numeric parameters from measurement, to syntactical descriptions of key features. The classification of leaf species shown in Chapter 11 is an example of the use of statistical tools and numeric values. Recognition is the process by which these tools are subsequently used to find a particular object or structure within an image. It functions at many different levels, including such different processes as finding a face in an image or matching that face to a specific individual.
