Data Structures, Computer Graphics, and Pattern Recognition

493 Pages·1977·25.614 MB·English

by  A. Klinger

Data Structures,
Computer Graphics,
and Pattern Recognition
Edited by
A. Klinger
University of California
Computer Science Department
Los Angeles, California
K. S. Fu
Purdue University
Department of Electrical Engineering
Lafayette, Indiana
T. L Kunii
University of Tokyo
Department of Information Science
Hongo Bunkyo, Tokyo
®
Academic Press, Inc.
NEW YORK SAN FRANCISCO LONDON 1977
A Subsidiary of Harcourt Brace Jovanovich, Publishers
COPYRIGHT © 1977, BY ACADEMIC PRESS, INC.
ALL RIGHTS RESERVED.
NO PART OF THIS PUBLICATION MAY BE REPRODUCED OR
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PERMISSION IN WRITING FROM THE PUBLISHER.
ACADEMIC PRESS, INC.
Ill Fifth Avenue, New York, New York 10003
United Kingdom Edition published by
ACADEMIC PRESS, INC. (LONDON) LTD.
24/28 Oval Road, London NW1
Library of Congress Cataloging in Publication Data
Main entry under title:
Data structures, computer graphics, and pattern
recognition.
Consists of papers, many of which evolved from
presentations given at the IEEE Computer Society con
ference held in Los Angeles, May 1975.
Includes bibliographies and index.
1. Data structures (Computer science)-Addresses,
essays, lectures. 2. Computer graphics-Addresses,
essays, lectures. 3. Pattern recognition systems-
Addresses, essays, lectures. I. Klinger, Allen,
Date II. Fu, King Sun, Date III. Kunii,
Toshiyasu.
QA76.9.D35D37 001.6'4 77-1598
ISBN 0-12-415050-0
PRINTED IN THE UNITED STATES OF AMERICA
List of Contributors
Numbers in parentheses indicate pages on which authors' contributions begin.
R. E. Barnhill (413),* University of Utah, Department of Mathematics, Salt
Lake City, Utah 84112
Charles M. Eastman (31), Carnegie-Mellon University, School of Urban & Public
Affairs, Schenley Park, Pittsburgh, Pennsylvania 15213
James D. Foley (239), Bureau of the Census, Graphics Software Branch, Wash­
ington, D.C.20233
K. S. Fu (449), Purdue University, Department of Electrical Engineering, West
Lafayette, Indiana 47907
Marilyn L. Griffith (309), Information Sciences, Electronics Research Division,
Rockwell International, 3370 Miraloma Avenue, P.O. Box 3105, Anaheim,
California 92803
R. A. Jarvis (273), The Australian National University, Department of Computer
Science, Canberra, A.C.T. 2600, Australia
T. Kasvand (179), National Research Council of Canada, Computer Graphics
Section, Radio & Electrical Engineering, Ottawa, Ontario, K1A OR8 Canada
Carolyn Kimme (427),* University of California, School of Engineering and
Department of Radiological Sciences, Irvine, California 92664
Allen Klinger (385), University of California, Computer Science Department,
3531 Boelter Hall, Los Angeles, California 90024
Bernard J. O'Loughlin (427), University of California, School of Engineering and
Department of Radiological Sciences, Irvine, California 92664
* Indicates first author
XI
XÜ LIST OF CONTRIBUTORS
Carl V. Page (213), Michigan State University, Computer Science Department,
East Lansing, Michigan 48824
Michael L. Rhodes (385),* University of California, Computer Science Depart­
ment, 3531 Boelter Hall, Los Angeles, California 90024
R. F. Riesenfeld (413), University of Utah, Computer Science Department, Salt
Lake City, Utah 84112
John P. Riganati (309),* Information Sciences, Electronics Research Division,
Rockwell International, 3370 Miraloma Avenue, P.O. Box 3105, Anaheim,
California 92803
Jack Sklansky (427), University of California, School of Engineering and Depart­
ment of Radiological Sciences, Irvine, California 92664
Frank W. Tompa (3), Department of Computer Science, University of Waterloo,
Waterloo, Ontario, Canada
Robin Williams (103,105,153,167),* International Business Machines, Monterey
and Cottle Roads, San Jose, California 95193
Charles T. Zahn, Jr. (59), Stanford Linear Accelerator Center, Computation
Research Group, Stanford, California 94305
indicates first author
Preface
Many researchers have seen the close connection between computer graphics
and pattern recognition and the central role of data structures in those fields.
Data structures are themselves an important subject of study in computer
science. Many data structure types and application areas use the special proper­
ties of them to facilitate computing algorithms. Among the main structuring
techniques are linear lists, trees, and binary trees. Distinctions between sequen­
tial and linked allocation of storage, the number and types of pointers, and the
modes of data organization (e.g., stacks, queues, trees, and lists) are all impor­
tant in planning software. This book focuses on the computer graphics and
pattern recognition applications of data structures methodology. It presents de­
sign related principles and research aspects of the computer graphics, system
design, data management, and pattern recognition tasks.
Computer graphics and pattern recognition, respectively, stand for synthetic
and analytic aspects of processing patterned data. In both computer graphics^and
pattern recognition, there is a core of dependence upon data structures and this
is the central subject of this book. In all programs written to do patterned data
processing, the data structure is a formal basis for organizing quantities that are
to be processed. Hence, the data structure is actually a common medium, one
that can join these two special research areas. Data structure design, selecting the
data's memory representation, is central to programs for creating graphics con­
sole displays. Data structures serve as "transformers" between complex data
relationships and serial processing systems, since they are links between a com­
plex pattern that humans may perceive in an input stream of patterned data and
the programs that will process that data.
Recent developments in computer technology have made available small
machines with graphic display capability. This has increased the tendency to
present the pattern recognition task as an interactive process where the computer
partially serves man as a reformatting tool. Since data structures theory con­
tributes to programming in the small memory context, this technological change
has also highlighted the need for using a common representation medium for the
display and analysis aspects of problems.
Rapid development of the pattern recognition and computer graphics technical
fields and their common use of data structure principles in organizing sophisti­
cated processing routines has led to an increasing awareness in the research
XIII
XIV PREFACE
community of the close relationship among these three topics. A conference
held in May 1975 in Los Angeles, in participation with the IEEE Computer
Society, brought together researchers working on all these themes. Over three
hundred scientists attended, and The Proceedings of the Conference on Com­
puter Graphics, Pattern Recognition, and Data Structure (IEEE, New York, 75
CHO 981-1C) includes ninety technical papers. While this conference was being
organized, the three editors conceived the need for a book that would focus on
the conference theme in a format allowing more extensive presentation of ex­
pository material. The intent, to provide a starting point for researchers inter­
ested in the intersection of the three fields, is realized by this volume. IVlany of
the papers included here evolved from papers presented at the 1975 conference,
and from discussions at the time of the conference between the editors jointly,
and with individual authors.
The material presented here will be of interest to practitioners in data struc­
tures, particularly those who are applying real computer systems to problems
involving image, speech, and medical data. The chapters convey current state-of-
the-art uses of data structures in computer graphics displays and pattern recogni­
tion algorithms. IVlany chapters conclude with exercises that will be useful to
students and to instructors who wish to use this book as a supplementary text.
Other chapters cover current research topics, including language understanding
and syntactic pattern recognition. Such material is not suitable for exercise
problems, but in keeping with many of the other chapters, this research litera­
ture provides topics for further research.
Finally, a word of thanks to all of the contributors for their care in preparing
their manuscripts for this volume, to R. Burns whose organizational skills are
reflected here, and to J. Badal and D. Trammel for their help with the typing. The
support of the Air Force Office of Scientific Research is gratefully acknowledged.
Data Structure Design
FRANK W. TOMPA
Department of Compute? Science
University of Waterloo
Waterloo3 Ontario Canada
Λ
1. LEVELS OF DATA REFINEMENT
Many data structures designers realize that data should be
specified at two levels: the abstract, user-oriented informa­
tion structure and the concrete, machine-oriented storage struc­
ture. Judging from the experience of algorithm designers, how­
ever, data structures should instead be viewed at many levels.
The design methodology suggested here is based on five
views of data:
•data reality: the data as it actually exists (in­
cluding all aspects which are typically ignored
except in philosophical treatises)
.data abstraction: a model of the real world which
incorporates only those properties thought to be
relevant to the application(s) at hand
•information structure: a refinement of the ab­
straction in which only some of the relevant data
relationships are made explicit, the others de­
rivable indirectly (usually through some computa­
tions involving relational composition)
•storage structure: a model of the information
structure, often expressed in terms of diagrams
which represent cells, linked and contiguous lists,
and levels of storage media
3
4 FRANK W.TOMPA
•machine encoding: a final computer representation,
including specifications for encoding primitive
data objects (e.g., the representations for
numbers and characters, the use of absolute as
opposed to relative addresses, and the use of
data packing).
The design of a data structure should proceed through successive
levels, binding only those aspects which are necessary to speci­
fy each level. Within a level, the process of stepwise refine­
ment should be adapted from algorithm design methodology to pro­
vide as smooth a transition as possible to the more detailed
data level [10, 17, 38].
These five views of data correspond exactly to the levels
of development in the corresponding program which describes the
data operations to be performed. The first step defines the
application's goal in terms of real objects. Next, the approach
to a solution which achieves that goal is specified in terms of
the data abstraction. At the third step, an algorithm incorpo­
rating the approach is designed to operate on the information
structure, after which a program implementing the algorithm is
written to manipulate data in terms of its storage structure.
Finally, at the fifth level, the program is translated into ob­
ject code which operates on data bound to a particular machine
encoding.
2. AN EXAMPLE OF STEPWISE DATA STRUCTURE DESIGN
Designing the data structure for a hypothetical genealogi­
cal application will serve to illustrate the methodology to be
followed. It will be assumed that a historical foundation wishes
to computerize the Habsburg family tree for a fact retrieval sys­
tem which is to be implemented on an IBM 360/75.
By the time the data structure is to be designed, the data
reality has already been chosen: in this case, the structure of
all the facts concerning the Habsburg lineage. In many cases,
the primitives available for the machine encoding level have
also been fixed: here, those provided by 360 architecture (e.g.
memory addressing structure, cell size). Thus the design pro­
cess is actually composed of four major steps which serve as
transitions, or mappings, from one data structure level to the
next.
DATA STRUCTURE DESIGN 5
The first step in the design is to identify the aspects of
the real world which are relevant to the application. Some facts
concerning the Hapsburgs are clearly of no concern to genealogi­
cal studies: for example, that the name is derived from Habichts­
burg on the Aar, that Albert III was landgrave of Upper Alsace,
or that Emperor Maximillian of Mexico did not get along very
well with his brother Emperor Francis Joseph I.* The data de­
signer must therefore consider the data reality and select those
properties to record; all other information will be impossible
to obtain from the data base. From discussions with the direc­
tors of the historical foundation, it may be learned that the
only relevant facts are persons' names, dates of birth and death,
and blood and marital relationships. This information consti­
tutes the data abstraction.
At the level of the data abstraction, all data is equally
accessible. In a manner similar to examining a conventional
genealogical chart, information can be obtained about any entry
without necessarily starting a search from a particular root
(e.g., Guntram the Rich). In addition, it must be as easy to
ascertain any relationships as any others; for example, at the
data abstraction level, finding all the second cousins of
Ferdinand III or the great grandchildren of Maria Theresa is con­
ceptually as natural as finding parents, children, a name, or a
date of birth. That is, there is no concept of efficiency yet
at this stage of the design.
After the data abstraction is designed, the next step is
to decide on an information structure by choosing which of the
relevant data relations will be stored explicitly and which will
remain accessible only via others. For example, it may be that
researchers often require the names of the Habsburg cousins of
certain Habsburgs. Done often enough, it would be convenient to
store the cousin relationship explicitly; thus, for example,
associated with Emperor Rudolph II is the set of nineteen cousins
(Figure 1). Storing cousins explicitly, however, results in
some redundancy, because sets of cousins often intersect. To
avoid such redundancy, cousins from different parents could be
kept separately, and thus cousins may be found by accessing the
children of a person's uncles and aunts. Instead of a simple
retrieval of one set, first the uncles and aunts of Rudolph
must be collected, yielding Philip II and Joan on his mother's
side and Ferdinand of Tirol, Charles, and 11 aunts on his father's
*This and all subsequent material concerning the Habsburgs, is
obtained from reference 13*
6 FRANK W.TOMPA
9 DAUGHTERS
2 OTHER SONS
DAUGHTERS
CHARLES JOSEPH
LEOPOLD OF TIROL
MAXIMILLIAN ERNEST
-^· FERDINAND Π
FERDINAND
**OF TIROL
9 OTHERS
*·· WENZEL
■»»· ALBERT
MAXIMILLIAN
**· MATHIAS
ERNEST
*>· RUDOLPH Π
CHARLES
Y
-+· PHILIP m
PHILIP II
2 DAUGHTERS
CHARLES
Figure 1: Detail From a Habsburg Genealogical Chart
side; then the union of their children must be found. Thus the
price of less redundancy is more complicated access. By examin­
ing the relative frequency of operations to be performed on the
data, the genealogical data designers choose to maintain for
each Habsburg, the name, parents, spouses, children, date of
birth, and date of death:
set of person Habsburg;
type person = (string NAME; elders PARENTS;
set of offspring FAMILY; life YEARS);
type elders = (person MOTHER, FATHER);
type offspring = (person SPOUSE; set of person CHILDREN);
type life = (date BIRTH, DEATH);