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## Über dieses Buch

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing presents a comprehensive introduction of the use of fuzzy models in pattern recognition and selected topics in image processing and computer vision. Unique to this volume in the Kluwer Handbooks of Fuzzy Sets Series is the fact that this book was written in its entirety by its four authors. A single notation, presentation style, and purpose are used throughout. The result is an extensive unified treatment of many fuzzy models for pattern recognition. The main topics are clustering and classifier design, with extensive material on feature analysis relational clustering, image processing and computer vision. Also included are numerous figures, images and numerical examples that illustrate the use of various models involving applications in medicine, character and word recognition, remote sensing, military image analysis, and industrial engineering.

## Inhaltsverzeichnis

### 1. Pattern Recognition

Abstract
There is no lack of definitions for the term pattern recognition. Here are a few that we like.
James C. Bezdek, James Keller, Raghu Krisnapuram, Nikhil R. Pal

### 2. Cluster Analysis for Object Data

Abstract
Figure 2.1 portrays cluster analysis. This field comprises three problems: tendency assessment, clustering and validation. Given an unlabeled data set, ① is there substructure in the data? This is clustering tendency — should you look for clusters at all? Very few methods — fuzzy or otherwise — address this problem. Panayirci and Dubes (1983), Smith and Jain (1984), Jain and Dubes (1988), Tukey (1977) and Everitt (1978) discuss statistical and informal graphical methods (visual displays) for deciding what — if any — substructure is in unlabeled data.
James C. Bezdek, James Keller, Raghu Krisnapuram, Nikhil R. Pal

### 3. Cluster Analysis for Relational Data

Abstract
In Chapter 1 we mentioned that two types of data, object (X) and relational (R), are used for numerical pattern recognition. Relational methods for classifier design are not as well developed as methods for object data. The most compelling reason for this is probably that sensors collect object data. Moreover, when each object is not represented by a feature vector, the problem of feature analysis is non-existent. Consequently, the models in this chapter deal exclusively with clustering. There are many applications that depend on clustering relational data — e.g., information retrieval, data mining in relational databases, and numerical taxonomy, so methods in this category are important. Several network methods for relational pattern recognition are given in Chapter 5.
James C. Bezdek, James Keller, Raghu Krisnapuram, Nikhil R. Pal

### 4. Classifier Design

Abstract
In Section 1.1 we defined a classifier as any function D:ℜp ↦ Npc. The value y = D(z) is the label vector for z in ℜP. D is a crisp classifier if D [ℜp] = Nhc; otherwise, the classifier is fuzzy, possibilistic or probabilistic, which for convenience we lump together as soft classifiers. This chapter describes some of the most basic (and often most useful) classifier designs, along with some fuzzy generalizations and relatives.
James C. Bezdek, James Keller, Raghu Krisnapuram, Nikhil R. Pal

### 5. Image Processing and Computer Vision

Abstract
Digital image processing is the study of theories, models and algorithms for the manipulation of images (usually by computer). It spans a wide variety of topics such as digitization, histogram manipulation, warping, filtering, segmentation, restoration and compression. Computer vision deals with theories and algorithms for automating the process of visual perception, and involves tasks such as noise removal, smoothing, and sharpening of edges (low-level vision); segmentation of images to isolate object regions, and description of the segmented regions (intermediate-level vision); and finally, interpretation of the scene (high-level vision). Thus, there is much overlap between these two fields. In this chapter, we concentrate on some of the aspects of image processing and computer vision in which a fuzzy approach has had an impact. We begin with some notation and definitions used throughout the chapter.
James C. Bezdek, James Keller, Raghu Krisnapuram, Nikhil R. Pal

### Backmatter

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