Über die Autorin bzw. den Autor

Roberto Brunelli, Senior Researcher, ITC-irst, Italy
Roberto Brunelli is currently working for ITC-irst for the Technologies of Vision Research Line of Interactive Sensory Systems Division. He has held this post since 1987 after gaining his degree in Physics from the University of Trento (Italy). His research activities and interests are in the areas of computer vision tools, analysis of aerial images, the development of algorithms for the compressed description of binary images, optimization, neural networks, face analysis, video analysis and image retrieval. Dr Brunelli's research projects have been implemented in several EU funded projects, and he has also undertaken teaching assignments at the International Doctorate School of the University of Trento. He has written over 30 published journal and conference papers, several of which deal with computational face perception. The paper 'Template Matching: Matched Spatial Filters and Beyond' received a Pattern Recognition Society Award in 1998. He has acted as a referee for some of the major journals on image processing and related techniques, for example Computer Vision and Image Understanding and IEEE Transactions on Image Processing, and has also been on the Technical Committee for several conferences, including Audio- and Video-Based Biometric Person Authentication, IEEE Conference on Computer Vision and Pattern Recognition and European Conference on Computer Vision.

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The detection and recognition of objects in images is a key research topic in the computer vision community. Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems. This book and the accompanying website focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications. Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli:

• examines the basics of digital image formation, highlighting points critical to the task of template matching;

• presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets;

• discusses recent pattern classification paradigms from a template matching perspective;

• illustrates the development of a real face recognition system;

• explores the use of advanced computer graphics techniques in the development of computer vision algorithms.

Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection. It is also of interest to graduate students undertaking studies in these areas.

Aus dem Klappentext

The detection and recognition of objects in images is a key research topic in the computer vision community. Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems. This book and the accompanying website focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications. Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli:
 
* examines the basics of digital image formation, highlighting points critical to the task of template matching;
 

* presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets;
 

* discusses recent pattern classification paradigms from a template matching perspective;
 

* illustrates the development of a real face recognition system;
 

* explores the use of advanced computer graphics techniques in the development of computer vision algorithms.
 

Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection. It is also of interest to graduate students undertaking studies in these areas.

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Template Matching Techniques in Computer Vision

John Wiley & Sons

Chapter One

Somewhere, somewhen, a two headed strategic meeting on face recognition and matters alike: t: What about using template matching? r: Template matching? t: Yes, a simple technique to compare patterns ... r: I'll have a look.

Faces' faces - r's virtual autobiography Roberto Brunelli

Go thither; and, with unattainted eye, Compare her face with some that I shall show, And I will make thee think thy swan a crow. Romeo and Juliet William Shakespeare

Computer vision is a wide research field that aims at creating machines that see, not in the limited meaning that they are able to sense the world by optical means, but in the more general meaning that they are able to understand its perceivable structure. Template matching techniques, as now available, have proven to be a very useful tool for this intelligent perception process and have led machines to superhuman performance in tasks such as face recognition. This introductory chapter sets the stage for the rest of the book, where template matching techniques for monochromatic images are discussed.

1.1. Template Matching and Computer Vision

The whole book is dedicated to the problem of template matching in computer vision. While template matching is often considered to be a very basic, limited approach to the most interesting problems of computer vision, it touches upon many old and new techniques in the field.

The two terms template and matching are used in everyday language, but recalling the definitions more closely related to their technical meaning is useful:

template/pattern

1. Anything fashioned, shaped, or designed to serve as a model from which something is to be made: a model, design, plan, outline.

2. Something formed after a model or prototype, a copy; a likeness, a similitude.

3. An example, an instance; esp. a typical model or a representative instance.

matching

1. Comparing in respect of similarity; to examine the likeness or difference of.

A template may additionally exhibit some variability: not all of its instances are exactly equal (see Figure 1.1). A simple example of template variability is related to its being corrupted by additive noise. Another important example of variability is due to the different viewpoints from which a single object might be observed. Changes in illumination, imaging sensor, or sensor configuration may also cause significant variations. Yet another form of variability derives from intrinsic variability across physical object instances that causes variability of the corresponding image patterns: consider the many variations of faces, all of them sharing a basic structure, but also exhibiting marked differences. Another important source of variability stems from the temporal evolution of a single object, an interesting example being the mouth during speech. Many tasks of our everyday life require that we identify classes of objects in order to take appropriate actions in spite of the significant variations that these objects may exhibit. The purpose of this book is to present a set of techniques by which a computer can perform some of these identifications. The techniques presented share two common features:

all of them rely on explicit templates, or on representations by which explicit templates can be generated;

recognition is performed by matching: images, or image regions, are set in comparison to the stored representative templates and are compared in such a way that their appearance (their image representation) plays an explicit and fundamental role.

The simplest template matching technique used in computer vision is illustrated in Figure 1.2. A planar distribution of light intensity values is transformed into a vector x which can be compared, in a coordinate-wise fashion, to a spatially congruent light distribution similarly represented by vector y:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1.1)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1.2)

A small value of d(x, y) or a high value of s(x, y) is indicative of pattern similarity. A simple variation is obtained by substituting the [L.sub.2] norm with the [L.sub.p] norm:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1.3)

If x is representative of our template, we search for other instances of it by superposing it on other images, or portions thereof, searching for the locations of lowest distance d(x, y) (or highest similarity s(x, y)).

The book shows how this simple template matching technique can be extended to become a flexible and powerful tool supporting the development of sophisticated computer vision systems, such as face recognition systems.

While not a face recognition book, its many examples are related to automated face perception. The main reason for the bias is certainly the background of the author, but there are at least three valid reasons for which face recognition is a valid test bed for template matching techniques. The first one is the widespread interest in the development of high-performing face recognition systems for security applications and for the development of novel services. The second, related reason is that, over the last 20 years, the task has become very popular and it has seen a significant research effort. This has resulted in the development of many algorithms, most of them of the template matching type, providing material for the book. The third reason is that face recognition and facial expression interpretation are two tasks where human performance is considered to be flawless and key to social human behavior. Psychophysical experiments and the evolution of matching techniques have shown that human performance is not flawless and that machines can, sometimes, achieve super human performance.

1.2. The Book

A modern approach to template matching in computer vision touches upon many aspects, from imaging, the very first step in getting the templates, to learning techniques that are key to the possibility of developing new systems with minimal human intervention. The chapters present a balanced description of all necessary concepts and techniques, illustrating them with examples taken from face processing tasks.

A complete description of the imaging process, be it in the case of humans, animals, or computers, would require a (very large) book by itself and we will not attempt it. Chapter 2 discusses some aspects of it that turn out to be critical in the design of artificial vision systems. The basics of how images are created using electromagnetic stimuli and imaging devices...