In contrast to classical image analysis methods that employ "crisp" mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks. However, a solid understanding of fuzzy processing requires a firm grasp of essential principles and background knowledge. Fuzzy Image Processing and Applications with MATLAB presents the integral science and essential mathematics behind this exciting and dynamic branch of image processing, which is becoming increasingly important to applications in areas such as remote sensing, medical imaging, and video surveillance, to name a few. Many texts cover the use of crisp sets, but this book stands apart by exploring the explosion of interest and significant growth in fuzzy set image processing. The distinguished authors clearly lay out theoretical concepts and applications of fuzzy set theory and their impact on areas such as enhancement, segmentation, filtering, edge detection, content-based image retrieval, pattern recognition, and clustering. They describe all components of fuzzy, detailing preprocessing, threshold detection, and match-based segmentation. Minimize Processing Errors Using Dynamic Fuzzy Set Theory This book serves as a primer on MATLAB and demonstrates how to implement it in fuzzy image processing methods. It illustrates how the code can be used to improve calculations that help prevent or deal with imprecisionwhether it is in the grey level of the image, geometry of an object, definition of an objects edges or boundaries, or in knowledge representation, object recognition, or image interpretation. The text addresses these considerations by applying fuzzy set theory to image thresholding, segmentation, edge detection, enhancement, clustering, color retrieval, clustering in pattern recognition, and other image processing operations. Highlighting key ideas, the authors present the experimental results of their own new fuzzy approaches and those suggested by different authors, offering data and insights that will be useful to teachers, scientists, and engineers, among others.
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Reviewer: Vladimir Botchev
Almost all of the literature on the application of fuzzy logic and set theory to image processing is in the form of edited collections of papers. While this may be suitable for keeping informed on the progress in the field, there are no textbooks on this subject. This book, however, fits that need. It provides, in a rather thin volume, enough theoretical and practical information to assist students and practitioners. It is written in the same clear and concise style of Ray's previous text on image processing [1]. The book consists of 11 chapters. As is often the case with textbooks on the application of a well-established theory to a seemingly unrelated field, the first few chapters introduce the theory and shed light on how the two areas are related. Chapter 1's introduction to fuzzy logic is by no means complete, with just enough background for the purposes of this text. Chapter 2 introduces basic image processing concepts, cast into the imprecise fuzzy set framework, including fuzzy geometry, fuzzy clustering, and fuzzy morphology. Chapter 3 focuses on fuzzy similarity measures that are useful for image segmentation, motion estimation, and other related tasks. By now in the text, the authors assume that the reader has been provided with enough background, and so chapter 4 starts with details of fuzzy set theory applications. The theme of this chapter is image preprocessing and filtering for enhancement, with an emphasis on contrast enhancement. Chapter 5, on fuzzy thresholding, introduces the most popular thresholding methods and fuzzy thresholding algorithms. Chapter 6 is concerned with the extraction of regions of interest in an image, which is important for detecting known objects in an image and tracking. First, it introduces the back-projection class of algorithms, followed by a description of fuzzy-based methods. Chapter 7 discusses edge detection and its fuzzy variants, perhaps the topic most commonly found in publications. First, the chapter introduces a few of the most popular methods, and then it details fuzzy-based methods. Chapter 8 presents an overview of and fuzzy algorithms for content-based image retrieval, an interesting application area. The theme of chapter 9, also popular and widely published in the literature, is fuzzy pattern classification. Although not much can be said about this topic in about 20 pages, the authors manage to successfully distill the essential information needed for a basic understanding of fuzzy partitioning and pattern classification. Chapter 10 discusses the application of fuzzy neural networks to remote sensing. Finally, chapter 11 lists several MATLAB programs that can be used in conjunction with the text's concepts. Unfortunately, the MATLAB code is printed in the book; instead, it should be available online or on a complementary CD. Overall, this is an ideal introduction to the application of fuzzy set methods to image processing. I recommend it to all aspiring image processing practitioners, and it is even appropriate for readers who are already seasoned in the field. Online Computing Reviews Service
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The spatial and rank (SR) orderings of samples play a critical role in most signal processing algorithms. The recently introduced fuzzy ordering theory generalizes traditional, or crisp, SR ordering concepts and defines the fuzzy (spatial) samples, .
CIMCA '05: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
This paper discusses the issue of applying the theory of intuitionistic fuzzy sets in the field of image processing. In order to carry out this task, we have to find intuitive ways to interpret and describe the inherent ambiguity and vagueness carried .
