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This book provides an introduction to fuzzy logic approaches useful in image processing. The authors start by introducing image processing tasks of low and medium level such as thresholding, enhancement, edge detection, morphological filters, and segmentation and shows how fuzzy logic approaches apply. The book is divided into two parts. The first includes vagueness and ambiguity in digital images, fuzzy image processing, fuzzy rule based systems, and fuzzy clustering. The second part includes applications to image processing, image thresholding, color contrast enhancement, edge detection, morphological analysis, and image segmentation. Throughout, they describe image processing algorithms based on fuzzy logic under methodological aspects in addition to applicative aspects. Implementations in java are provided for the various applications.

Inhaltsverzeichnis

Frontmatter

Fundamentals of Fuzzy Image Processing

Frontmatter

Chapter 1. Image Representation Using Java

Abstract
This chapter covers some basic concepts of gray-level and color image representation. Digital images are logically represented using a matrix of elements, each element having a single value in case of gray-level images and three/four values in case of color images. The chapter also introduces the most used color models and the representation of images provided by Java.
Laura Caponetti, Giovanna Castellano

Chapter 2. Low-Level Image Processing

Abstract
This chapter covers some basic concepts of low-level image processing. It introduces fundamental methods for two primary image processing tasks, namely contrast enhancement, image smoothing, and edge detection. The chapter also introduces methods of function optimization for searching the optimal configuration of edge points.
Laura Caponetti, Giovanna Castellano

Chapter 3. Basics of Fuzzy Logic

Abstract
In this chapter, foundations of fuzzy logic are presented to introduce the necessary notations used throughout the following chapters. The chapter provides basic notions of fuzzy set theory and fuzzy systems, such as fuzzification, fuzzy rule base and inference engine, defuzzification, and fuzzy models.
Laura Caponetti, Giovanna Castellano

Chapter 4. Fuzzy Image Processing

Abstract
The use of fuzzy logic for image processing has led to the development of a wide range of techniques casting in the area of Fuzzy image processing. Fuzzy image processing consists of all those approaches that understand, represent, and process an image, its segments and/or its features as fuzzy sets. This chapter covers some basic concepts of fuzzy image processing, namely image fuzzification, image defuzzification and fuzziness measures. The chapter shows also that an image can be considered as an array of fuzzy sets having a membership function that denotes the degree of some image properties satisfied by the image pixels.
Laura Caponetti, Giovanna Castellano

Chapter 5. Java for Image Processing

Abstract
This chapter covers some fundamental concepts of Object-Oriented programming in Java. Fundamental classes of the Java packages java.awt and java.applet for image processing are presented. Moreover, this chapter introduces the concept of plugins in ImageJ and its on-board tools for plugin development. It starts with the discussion of the code skeleton of a new plugin and the sample plugins that are part of the ImageJ distribution, and covers those parts of the ImageJ API, which are essential for writing plugins, with a special focus on the image representation.
Laura Caponetti, Giovanna Castellano

Application to Image Processing

Frontmatter

Chapter 6. Color Contrast Enhancement

Abstract
In this chapter, we present the basic concepts of color contrast enhancement and focus on the use of fuzzy logic as a valid tool to enhance color images. In particular, we show how to define a fuzzy rule-based system for color image enhancement. An application to real-world color images is presented.
Laura Caponetti, Giovanna Castellano

Chapter 7. Image Segmentation

Abstract
This chapter deals with the methods of region-based image segmentation. It introduces some basic concepts such as definition of pixel neighbors, connectivity of a region, and the image segmentation problem. This chapter also describes clustering methods as powerful tools for image segmentation. Two application examples using clustering for color image segmentation and texture segmentation are provided.
Laura Caponetti, Giovanna Castellano

Chapter 8. Morphological Analysis

Abstract
Fuzzy mathematical morphology is an extension of binary morphology to gray-scale images using techniques from fuzzy logic. Fuzzy mathematical morphology can be applied to process image data having characteristics of vagueness and imprecision. In this chapter, the main concepts from fuzzy mathematical morphology are briefly introduced and the results of applying fuzzy morphological operators to construct morphological gradient are reported in low-contrast biological images.
Laura Caponetti, Giovanna Castellano

Chapter 9. Image Thresholding

Abstract
This chapter deals with the methods for image thresholding. It introduces some basic concepts such as two-level and multilevel thresholding. The chapter also describes one of the main classical nonfuzzy thresholding methods—the Otzu method—and presents the Huang method based on minimization of fuzzy entropy. An application to document image analysis, using fuzzy techniques for segmentation and neuro-fuzzy system for classification is provided.
Laura Caponetti, Giovanna Castellano

Backmatter

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