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Traditionally, three-dimensional image analysis (a.k.a. computer
vision) and three-dimensional image synthesis (a.k.a. computer
graphics) were separate fields. Rarely were experts working in one
area interested in and aware of the advances in the other. Over the
last decade this has changed dramatically, reflecting the growing
maturity of each of these areas. The vision and graphics
communities are today engaged in a mutually beneficial exchange,
learning from each other and coming up with new ideas and
techniques that build on the state of the art in both fields. This
book is the result of a fruitful collaboration between scientists
at the University of NA1/4rnberg, Germany, who, coming from diverse
fields, are working together propelled by the vision of a unified
area of three-dimensional image analysis and synthesis. Principles
of 3D Image Analysis and Synthesis starts out at the image
acquisition end of a hypothetical processing chain, proceeds with
analysis, recognition and interpretation of images, towards the
representation of scenes by 3D geometry, then back to images via
rendering and visualization techniques. Coverage includes
discussion of range cameras, multiview image processing, the
structure-from-motion problem, object recognition, knowledge-based
image analysis, active vision, geometric modeling with meshes and
splines, and reverse engineering. Also included is cutting-edge
coverage of texturing techniques, global illumination, image-based
rendering, volume visualization, flow visualization techniques, and
acoustical imaging including object localization from audio and
video. This state-of-the-art volume is a concise and readable
reference for scientists, engineers, graduate students and
educators working in image processing, vision, computer graphics,
or visualization.
Figure 1.1. An outdoor scene "A bus is passing three cars which are
parking between trees at the side of the road. Houses having two
storeys are lined up at the street. 3 4 Introduction Figure 1.2. An
assembly scene There seems to be a small open place between the
group of houses in the foreground and the store in the background."
In such or a similar way the content of the natural scene shown
above can be described. It is quite easy to give such a short
description. The problem is somewhat more complex for the second
image. First of all, it can be stated that the image does not show
an everyday scene. It appears as a kind of man made surrounding.
But everyone can accept the following statements about this image:
1. The image shows a snapshot of an assembly line. 2. The robot in
front is screwing. 3. There is no person in the working area of the
robots. 4. All objects on the conveyor belt are worked on by
robots. There are no free objects on the belt.
Traditionally, say 15 years ago, three-dimensional image analysis
(aka computer vi sion) and three-dimensional image synthesis (aka
computer graphics) were separate fields. Rarely were expert"
In this second edition every chapter of the first edition of
Pattern Analysis has been updated and expanded. The general view of
a system for pattern analysis and understanding has remained
unchanged, but many details have been revised. A short account of
light and sound has been added to the introduction, some
normalization techniques and a basic introduction to morphological
operations have been added to the second chapter. Chapter 3 has
been expanded significantly by topics like motion, depth, and shape
from shading; additional material has also been added to the
already existing sections of this chapter. The old sections of
Chap. 4 have been reorganized, a general view of the classification
problem has been added and material provided to incorporate
techniques of word and object recognition and to give a short
account of some types of neural nets. Almost no changes have been
made in Chap. 5. The part on representation of control structures
in Chap. 6 has been shortened, a section on the judgement of
results has been added. Chapter 7 has been rewritten almost
completely; the section on formal grammars has been reduced, the
sections on production systems, semantic networks, and knowledge
acquisition have been expanded, and sections on logic and
explanation added. The old Chaps. 8 and 9 have been omitted. In
summary, the new edition is a thorough revision and extensive
update of the first one taking into account the progress in the
field during recent years.
Figure 1.1. An outdoor scene "A bus is passing three cars which are
parking between trees at the side of the road. Houses having two
storeys are lined up at the street. 3 4 Introduction Figure 1.2. An
assembly scene There seems to be a small open place between the
group of houses in the foreground and the store in the background".
In such or a similar way the content of the natural scene shown
above can be described. It is quite easy to give such a short
description. The problem is somewhat more complex for the second
image. First of all, it can be stated that the image does not show
an everyday scene. It appears as a kind of man made surrounding.
But everyone can accept the following statements about this image:
1. The image shows a snapshot of an assembly line. 2. The robot in
front is screwing. 3. There is no person in the working area of the
robots. 4. All objects on the conveyor belt are worked on by
robots. There are no free objects on the belt.
Der vorliegende Band behandelt Verfahren der Kunstlichen
Intelligenz (KI) in der Bild- und Sprachanalyse, also in einem
Teilgebiet der Mustererkennung (ME). Die Definition und Abgrenzung
von Begriffen wie KI und ME wird in der Literatur nicht einheitl
ich gehandhabt; es ist aber wichtig daran zu erinnern, dass beide
aus ihrer Fruhzeit gemeinsame Wurzeln haben. Die Fahigkeit zur
Erkennung von Mustern, und ganz allgemein zur Wahrnehmung der
Umwelt mit geeigneten Sensoren, wurde als wesentl i che Vorau
ssetzung fur autonom agi erende "i nte 11 i gente" tech nische
Systeme angesehen. Einerseits wurde in ersten Veroffentlichungen
uber KI immer wieder die ME als ein zu losendes Problem genannt,
andererseits wurde als wichtiges Problem in der ME die Einbeziehung
von Wissen uber das Problem und die zu erkennenden Muster
gefordert. Die Wichtigkeit der Wissensverarbeitung in der ME lasst
sich in Forschungsantragen schon aus dem Ende der funfziger Jahre
nach lesen. Zu dieser Zeit war der Stand der ME naturl ich noch
nicht so weit ent wickelt wie heute, er stand praktisch auf der
Stufe der Extraktion und Klassifi kation von Merkmalvektoren.
Leider wird immer noch vielfach ME mit diesem An fangsstand der
Technik verwechselt und nicht die generelle Aufgabe, namlich die
automatische Verarbei tung, Auswertung und Interpretation
sensorischer Informa tion gesehen. Inzwi schen sind auf dem Sektor
der Wi ssensverarbeitung in der KI solche Fortschritte erzielt
worden, dass man in der ME die Nutzung von Wi ssen nicht mehr nur
fordern sondern tatsachl ich auch durchfuhren kann."
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