TABLE OF CONTENTS Morphological image processing is a tool for extracting or modifying information on the shape and structure of objects within an image. Morphological operators, such as dilation, erosion and skeletonization, are particularly useful for the analysis of binary images, although they can be extended for use with grayscale images. Morphological operators are non-linear, and common usages include filtering, edge detection, feature detection, counting objects in an image, image segmentation, noise reduction and finding the mid-line of an object.
After reading this chapter you will be able to:
describe three different ways to define distance in a digital image;
outline the algorithms for the main morphological operators;
choose the appropriate morphological operator, or series of operators, to perform certain processing tasks, such as noise reduction and object separation;
use the appropriate structuring elements in a hit-or-miss transform to detect simple shapes;
distinguish between skeletonization and the medial axis transform;
discuss the applications of morphological processing to grayscale images;
implement the appropriate morphological operations for various processing tasks.
The field of mathematical morphology contributes a wide range of operators to image processing, all based around a few simple mathematical concepts from set theory and, in the case of binary images, ( Boolean) logic operations such as AND, OR, XOR (exclusive OR) and NOT. The union operation, A ?B, for example, is equivalent...
