Chapter 6 - Binary digital image characteristics

Abstract

This chapter introduces the basis for the shape characterization of an image by detailing the common characteristics considered in image processing. It considers and studies the relationships of such characteristics with noise and presents definitions and algorithms in this context. In the model of a binary digital image, the two subsets of pixels are commonly defined within the image: the foreground (i.e., black pixels) and the background (i.e., white pixels). Binary digital image analysis is concerned with the characterization of properties in the set of foreground pixels. This chapter introduces the morphological study of the sets of discrete points representing pixels in a binary image. This work relies on the definition of connected components, which itself depends on the definition of a neighborhood for a pixel. The chapter concentrates on the neighborhoods defined on square lattices. It is apparent that extensions to other lattices are straight forward in most cases. Once components are characterized, the definition of morphological factors allows for their analysis at a global level. The classifications of components for further recognition processing can be achieved using such factors. The chapter also introduces the component-labeling problem. After such a processing step, each connected component is treated as a separate part of the image and forms the basic entity for morphological study. However, before initiating such a study, it may be necessary to remove redundant or unwanted information from a component. Such information is referred to as noise and methods for reducing it are presented.