Computational Visualization: Graphics, Abstraction and Interactivity

463 Pages·1999·17.09 MB·English

by  Prof. Dr. Thomas Strothotte (auth.)

Computational Visualization:
Graphics, Abstraction, and Interactivity
Springer
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Thomas Strothotte
Computational
Visualization
Gr a ph ics, Abstraction,
and Interactivity
Foreword by K. van Overveld
With the collaboration of
Oliver Deussen, Frank Godenschweger,
Je rg Hamel, Ralf Helbing, Axel Hoppe,
Kathrin Ludicke, Maic Masuch,
Rainer Michel, Ian Pitt, Bernhard Preim,
Andreas Raab, Alf Ritter, Michael Ruger,
Jerg R. J. Schirra, Stefan Schlechtweg,
Martin Scholz, Kornelia Ullrich,
and Hubert Wagener
Springer
Prof. Dr. Thomas Strothotte
Otto-von-Guericke University of Magdeburg
Faculty of Computer Science
UniversiHitsplatz 2, D-39106 Magdeburg, Germany
E-mail: [email protected]
With 214 Figures and 11 Tables
ISBN-13: 978-3-642-64149-7 Springer-Verlag Berlin Heidelberg New York
ACM Computing Classification (1998): 1.3,1.7.5, H.5.1-2, K.3, J.3
Library of Congress Cataloging-in-Publication Data
Computational Visualization: Graphics, Abstraction, and Interactivityl
Thomas Strothotte. p. cm. Includes bibliographical references and index.
ISBN-13: 978-3-642-64149-7 (hardcover: aile paper)
1. Computer graphics. 2. Abstraction. 3. Interactive computer systems.
I. Strothotte, Thomas, 1959-. T385.A273 1998 006.6-dc21 98-26278 CIP
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permitted only under the provisions of the German Copyright Law of September 9,
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Springer-Verlag. Violations are liable for prosecution under the German Copyright
Law.
ISBN-13: 978-3-642-64149-7 e-ISBN-13: 978-3-642-59847-0
DOl: 10.1007/978-3-642-59847-0
© Springer-Verlag Berlin Heidelberg 1998
Softcover reprint of the hardcover I st edition 1998
The use of designations, trademarks, etc. in this publication does not imply, even in
the absence of a specific statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
Cover Design: Kunkel + Lopka, Heidelberg
Typesetting: Camera-ready by authors
Printed on acid-free paper SPIN 10649026 45/3142 543210
Dedicated to
Rul Gunzenhauser
on the occasion of his
65th birthday in September, 1998
Thomas Strothotte
reword
Many computer applications deal with communication. Far over twenty
years, the use of text processors has been common practice in office envi
ronments. The field of computer graphics, one of the dominant fields in
computer science, deals with the role of computers in (visual) communica
tion. The emphasis of much of this work has been on the techniques of image
generation, and efficient graphical interaction for a variety of application do
mains. It is interesting to note that, in the computer science community,
relatively little attention has been paid to the underlying semantics of the
communicated pictorial messages. This may be partly understood from the
fact that the semantics of a message, communicated by computers, is not very
different from a similar message communicated without computer assistance.
Semioticsl has developed largely as a separate discipline from computer sci
ence.
A second reason for the long-lasting mutual ignorance between computer
science (and computer graphics in particular) and semiotics may be that
it requires sophisticated computer algorithms to represent the semantics of
images. Until quite recently, the algorithms of image generation in them
selves were sufficiently challenging to absorb the majority of the efforts in
the field. Also, computer architecture and programming sophistication were
not sufficiently developed to deal with the complicated task of representing
the meaning of the rendered images.
In the next few years there will be an ongoing research effort in main
stream computer graphics, focussing, e.g., on hardware architectures for real
time rendering and increasingly advanced paradigms for geometric modeling
and virtual reality. However, there is also a relatively new trend within com
puter graphics in which visual communication and interaction are indeed tied
in with an explicit attention for the meaning of the communicated images.
This relatively young branch of computer science is called Computational
Visualistics.
Background
In order to understand the necessity of linking the visual aspects of an image
with its meaning, which is one of the starting points of computational visu-
Semiotics: " ... the study of patterned [. .. ] communication behavior, including [. .. ]
signs and symbols." [Web90]
VIII Foreword
alistics, it may be illustrative to study an example first. Let us look at the
evolution of the Western alphabet. Although the alphabet developed sepa
rately from the notion of computers, it is instructive to become aware of some
of the issues regarding the representation of meaning that underly (dealing
with) images - even if these images are just the individual characters of the
alphabet.
In the Western alphabet, every letter is a little drawing, a distribution of
dark on a light background, the result of many centuries of gradual trans
formations and artistic font design originating in ancient pictograms. For
instance, the "A" shape apparently originated in an Egyptian hieroglyph of
an eagle (ahom) in cursive hieratic writing.
A naturalistic drawing of an eagle is capable of conveying many anatomi
cal attributes of an individual bird. However, in some cases the drawing does
not refer to an individual bird, but to the more abstract notion of "an eagle"
in general, for instance in a pamphlet that has the purpose of instructing
shepherds to watch out for eagles. Now the artist responsible for drawing
this "generic eagle" faces a conceptual difficulty. Which instance of the class
"eagle" should he choose? To give a full account of the potential danger of
eagles for sheep, the artist really should refer to all possible eagles in all
possible postures. But it is clearly impossible to render this multitude of
drawings.
Interestingly, it is not only impossible to draw all individual postures
of all possible eagles, it is also unnecessary. In virtue of a powerful but
mysterious mental process in the observer's mind, a drawing of one particular
eagle, in one particular posture will automatically associate with the class
of all possible eagles2 in all possible postures. This process, which we will
call implicit generalization is a peculiar phenomenon. Implicit generalization
is nothing that has to be negotiated between the artist and the observer.
It occurs automatically, and it is probably one of the reasons that human
beings do rather well in an environment where successful recognition and
classification of similar, but different things (food, enemies, mates, etc.) is
critical in order to survive.
In the case of pictorial artifacts, however, implicit generalization takes on
more baroque forms. One reason for this is that not all artists are capable
of the perfect impressions3 we assumed above. And even a skilled artist is
sometimes in a hurry. So undoubtedly, less perfect drawings of eagles have
been around, some with wrong proportions, some with few details, indeed,
some highly schematic.
Implicit generalization in the case of the pragmatic meaning of an eagle
as a potentially dangerous large bird of prey may carryover to other species,
2 note: not only all existing eagles, but even all possible eagles!
3 We could call such impressions "photorealistic", although in this context the invention
of photography was still some 3900 years in the future and the usage of the word
"photorealistic" in the sense now used by computer graphics practitioners yet another
hundred years later ...
Foreword IX
such as falcons or buzzards. But there is a limit to the scope of this gener
alization. The abstraction will probably never include larks or blackbirds, or
leopards or wolfs.
The implicit generalization ofthe pragmatic meaning of pictures, however,
is much more "contagious". First, this may be due to the imperfection of the
picture. If the artist leaves out sufficient details the observer may be able to
make a successful mental match between the picture and all creatures with
wings, including lark and blackbirds, but also bats and dragon-flies. Or even
with all roughly V-shaped objects, depending on the simplicity of the picture.
But implicit generalization does not even stop here. After some time, the
schematic picture of the eagle may also refer to the word "eagle" instead of
the object "eagle", and somewhat later to the sound of the word "eagle", and
even later to the sound of the first letter of this word. And we have to realize
that all these abstractions (or rather changes in the communication code)
probably did not result from meticulously negotiated agreements between
the artist and the observer. On the contrary, they most likely occurred
largely unnoticed, and it would be interesting to know how much confusion
was caused during the process.
Apart from implicit generalizations, and other highly confusing meaning
transformers, there is a second process going on that complicates the relation
between pictures and the real world. The pictures themselves also seem to
evolve according to their own, hidden laws, of which "changing aesthetics"
and "artistic innovation" are merely the least mysterious ones. As a result,
the implicitly generalized eagle has not only evolved into an "A", but also an
A, an A, an Ill, an A, and an A and to many hundred more distributions of
dark on a light background.
Over the last 4000 years, the process of creating and transforming pictures
and endowing them with evolving meaning, either concrete or abstract or a
mixture of the two, has been a process of gradual evolution interspersed, every
now and then, with a quantum leap (the introduction of phonetic alphabets,
the invention of perspective, the usage of schematic drawings, the printing
process, photography, impressionism, expressionism, abstract painting, ... ).
Each time, undoubtedly, there was some confusion after each jump, but the
time lapse before the next one mostly allowed sufficient habituation to the
new communication codes. Until the last couple of decades of the second
millennium AD, when computers came along and everything started evolving
in accordance with MOORE's law.4
In particular, the developments in computer graphics have caused a tre
mendous increase in the number of available options for visualizing both
concrete and abstract information. But the focus of this work has been
predominantly on the picture-making process (to be more precise: on the
process of algorithmically imitating the physics of photography), and the ties
4 The amount of computing power available per dollar's worth of hardware doubles every
eighteen months.
X Foreword
betw-een the pictures and their underlying semantics, as well as alternative
strategies for picture generation, have been largely ignored. One lesson to
be learned from our small thought experiments on the history of semiotics,
outlined above, is that confusion is to be expected if the relation between
pictures and their meaning is left unspecified, and new communication codes
are introduced before the previous ones have been established.
Visual Representation and Meaning
As we mentioned in the introduction, the new research field of computational
visualistics attempts to fill in some of the blank areas that have been left open
by mainstream computer graphics research. The central theme in most of the
research in computational visualistics has to do with the relation between the
visual representation of images and their meaning. This relation is really two
directional:
l. The (visual) contents of an image should be such that this meaning is
optimally communicated: the meaning dictates (part of) the pictorial
contents;
2. Given the contents of an image, additional (non-graphical) communica
tion modes can be used to support conveyance of its meaning: the pictorial
contents dictate non-graphical attributes.
In the first category, there is a rich tradition, dating back to the pre-computer
era, where non-photorealistic imaging techniques have been used. In fact, our
short excursion to the origin of the ·Western alphabet is an example. Other
examples are simplified drawings, or drawings in which deliberate alterations
with respect to the depicted object(s) have been introduced. These drawings
are often used in the context of teaching or instruction. The alterations may
range from subtle modifications (e.g., local adjustments to the line style, size,
or amount of detail), to completely symbolic renderings (organization charts,
graphs). Halfway between these extremes we find geographic maps, architec
tural drawings and sketches, and so on. In many cases, the designer of these
types of images is not, in an algorithmic sense, aware of the types of alter
ations he or she applies. Therefore, in order to program computers to render
these types of images, one first has to carefully analyze and formalize the
process of non-photorealistic picture-making. It has to be fully understood
what choices a human artist would make when choosing levels of detail, line
styles, hatching, and so on, as a function of the structure and meaning of
the object or scene to depict. Next, there is the more technical challenge of
formalizing these choices, and translating them into suitable algorithms.
With respect to the second category, we observe that in some cases even
a photorealistic picture on its own is not sufficient to convey the intended
meaning. An example would be where the picture is meant to be understood
by a visually handicapped person; another example would be a situation
in which non-graphical references to the objects in the picture are required
Foreword XI
(e.g., from an associated text). In both cases, a non-pictorial annotation
is required, either in the form of a full non-graphical representation (e.g.,
a 1-D graph that is translated to a varying sound over time, or a tactile
map), or in the form of well-placed textual labels or symbolic icons. In many
applications we are confronted with the challenge of automating the process
of non-graphical annotation, based on a formal representation of the picture's
contents.
The two research fields above can be seen to enhance, extend, and build
on traditional techniques in image making. Although this is nowadays much
too laborious for large volume production, a human artist could in principle
generate many of the visual effects discussed above. There are further re
search fields, however, where computer graphics have introduced drastically
new paradigms in non-photorealistic image production. These are techniques
such as selective zooming (enlarging those portions of an image of a 3-D
model that are of current interest); manipulating the perspective of such im
ages; (non-photorealistic) animation; synthetic holograms, and others. These
are all techniques that only have become available over the last few years.
Perspectives
This book discusses in depth many of the research fields outlined above. It is
an anthology of some of the results that have been obtained in Computational
Visualistics in the University of Magdeburg over the last two years. Apart
from the direct relevance of the methods and techniques, described in the
sequel, much of the merit of this work lies in the dawning awareness that
effective visual communication critically relies on both the pictorial and the
semantic aspects of the communicated picture.
It is my sincere wish that this awareness will continue to inspire fruitful
and groundbreaking research.
Eindhoven, The Netherlands, August 1998 K ees van Overveld