PROCEDIMIENTOS Y CRITERIOS DE EVALUACIÓN

The third volume of the Handbook on Computer Vision and Appli- cations is centered on system aspects and applications. The system aspect actually has two meanings. First, there is the question of how all components that make up a computer vision system and that have been discussed in the first two volumes of the handbook are put to- gether as a system in an optimal way. Second, in real applications, a visual task is only part of a larger system. Thus, there is the question of how it is integrated in the best way.

This introduction covers some basic questions of the architecture of computer vision systems in Section1.1. Is it different from general- purpose computer systems? What are its basic function modules and what are the critical performance parameters?

Parts II and III of this volume are devoted to industrial, technical, and scientific applications of computer vision. This collection of appli- cation reports raises some important questions for theoreticians and practitioners alike. A theoretician may ask himself why his technique has not found its way into real-word applications. Or he may find out that his research tackles problems no one is interested in. Conversely, the practitioner may ask himself why he is still sticking with his inef- ficient and old-fashioned techniques when there are much better avail- able.

This volume creates an opportunity for the necessary dialogue be- tween basic research and applications. It is also the hope that the solu- tions presented in the application reports turn out to be useful in other application areas.

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Handbook of Computer Vision and Applications Copyright © 1999 by Academic Press

Volume 3 All rights of reproduction in any form reserved.

2 1 Introduction

1.1 Computer vision architecture

It is necessary to assume at the outset that no image acquisition or display is needed. Is there then any difference in the architecture of a computer vision system from a general computer? Yes? No? The answer is no, as a general-purpose computer is built only to be used to compute a wide range of tasks. It is thus only a question of how effi- ciently modern personal computer (PC) and workstation architectures can be used for computer vision tasks. The answer is yes, as for many applications it is required to put a stream of image data at a specified rate through the computer system. This requires so-called real-time computer hard- and software. Nowadays three trends are observable with respect to the platforms for computer vision applications.

Standard PCs. In recent years there has been a swift move away from dedicated—especially VME-bus based—computer system to standard PC-based systems for industrial applications. They have reached critical performance levels with respect to computing power and memory bandwidth for image processing (Section 3.2 and Chap- ter 11). This trend will only be accelerated in the near future by the addition of multimedia instruction sets, which make PCs more suitable for the processing of pixel data (Chapter3).

Intelligent cameras. Rather simple tasks can be performed by so-called intelligent cameras. The camera includes the entire hardware to process the acquired images in a standalone system. Examples of applications with such systems are discussed in Chapter13.

Configurable hardware. Field programmable gate arrays have acquired enough computing power to be suitable for sophisticated image pro- cessing (Chapter2).

Computer architecture for computer vision systems would be in- complete if only hardware were to be considered. The best hardware is only as good as the software running on it. One may argue that the long tradition in imaging of dedicated hardware has considerably hindered progress in software. Portable, modular, and reusable software is es- pecially critical for computer vision tasks because they tend to include many different modules. This is why three chapters in this volume deal with software engineering for computer vision (Chapters4–6). In ad- dition, a fourth chapter discusses application-oriented assessment of computer-vision algorithms (Chapter7).

Although a PC with a frame grabber and a camera can be regarded as a simple computer vision system, it generally is more than that. This can be seen by comparing computer vision systems with human or other biological vision systems. While it makes not much sense to imitate a biological system with a technical system, it is very useful to compare them on the basis of function modules as shown in Table1.1.

1.1 Computer vision architecture 3

Table 1.1: Function modules of human and machine vision Task Human vision Machine vision

Visualization Passive, mainly by re- flection of light from opaque surfaces

Passive and active (controlled il- lumination) using electromagnetic, particulate, and acoustic radiation (Volume 1, Chapters3and6) Image

formation

Refractive optical sys- tem

Various systems

(see Volume 1, Chapter4) Control of

irradiance

Muscle-controlled pupil Motorized apertures, filter wheels, tunable filters

Focusing Muscle-controlled change of focal length

Autofocus systems based on vari- ous principles of distance measure- ments

Irradiance resolution

Logarithmic sensitivity Linear sensitivity, quantization be- tween 8- and 16-bits; logarith- mic sensitivity, for example, HDRC- sensors (Volume 1, Chapter8) Tracking Highly mobile eyeball Scanner and robot-mounted cam-

eras (Chapter9) Processing and analysis Hierarchically organized massively parallel processing

Serial processing still dominant; parallel processing not in general use

From the table it is immediately clear that a camera and a computer are only two parts of many in a computer vision system. It is also obvi- ous that computer vision systems have a much richer variety and more precise function modules with respect to visualization, image forma- tion, control of irradiance, focusing, and tracking. Thus, a camera with a PC is a poor vision system from the system perspective. It has no way to control illumination, adapt to changes in the illumination, move around to explore its environment, track objects of interest or zoom into details of interest.

Control of illumination is the basis of many powerful technical tech- niques to retrieve the 3-D structure of objects in images. This in- cludes shape from shading and photometric stereo techniques (Vol- ume 2, Chapter19), active illumination techniques (Volume 1, Chap- ters 18 and 20). The active vision paradigm (Chapter 9) emphasizes the importance of an active response of the vision system to the ob- served scene, for example, by tracking objects of interest. Even more, many visual tasks can only be solved if they are operated in a closed loop, that is, an action is observed and controlled by a vision system in a feedback loop. This is known as the perception-action-cycle (Chap- ter10).

4 1 Introduction Table 1.2:Classification of tasks for computer vision systems

Task References

2-D geometry

Position Chapters31

Distance Chapters40

Size, area Chapters Chapter29

Form & shape Volume 2, Chapter 21; Chapters 8, 12, 19,41

Radiometry-related

Reflectivity Volume 2, Chapter19

Color Chapters8,27,40

Temperature Volume 2, Chapters2and5; Chapters35, 36

Fluorescence Volume 1, Chapter12; Chapters30,34, 39,40,41

Spatial structure and texture

Edges & lines Volume 2, Chapter10; Chapters8,28 Autocorrelation function

Local wave number; scale Volume 2, Chapter4; Chapters8 Local orientation Volume 2, Chapter10; Chapters8 Texture Volume 2, Chapter12; Chapters13

High-level tasks

Segmentation Volume 2, Chapter21; Chapters12,29, 31,37

Object identification Chapters12,13 Object classification Chapters8,13,37 Character recognition (OCR) Chapters14 Model- and knowledge-based

recognition and retrieval

Chapters13,25,29