Table Of ContentPATTERN RECOGNITION
IN PRACTICE II
Proceedings of an International Workshop
held in Amsterdam, June 19-21,1985
edited by
Edzard S. CELSEMA
Department of Medical Informatics ?
Free University, Amsterdam
and
Laveen N. KANAL
Department of Computer Science
University of Maryland, College Park, Md.
m
H
m
1986
NORTH-HOLLAND
AMSTERDAM · NEW YORK · OXFORD
© Elsevier Science Publishers B v., 1986
All rights reserved. No part of this publication may be reproduced, stored in a retrieval
system, ortransmitted, in any form or by any means, electronic, mechanical, photocopying,
recording or otherwise, without the prior permission of the copyright owner.
ISBN: 0 444 87877 7
Publishers:
ELSEVIER SCIENCE PUBLISHERS B.V.
P.O. Box 1991
1000 BZ Amsterdam
The Netherlands
Sole distributors for the U.S.A. and Canada:
ELSEVIER SCIENCE PUBLISHING COMPANY, INC.
52 Vanderbilt Avenue
New York, NY. 10017
U.S.A.
Library of Congress Cataloging-in-Publication Data
Main entry under title:
Pattern recognition in practice II.
Includes bibliographies and indexes.
1. Pattern perception—Congresses. 2. Image
processing—Congresses. 3. Depth perception—
Congresses. I. Gelsema, Edzard S., 1937-
II. Kanal, Laveen N.
Q32T.P3T8 1986 006.1+ 05-252^7
ISBN 0-l+U*-87877-7 (U.S.)
PRINTED IN THE NETHERLANDS
PREFACE
This book contains most of the papers presented at the conference "Pattern
Recognition in Practice II" held in Amsterdam on June 19-21, 1985. This conference
was organized to bring together scientists doing research in pattern recognition
methodology and those interested in specific practical applications. Many of the
participants are deeply involved in both technical development and one or more
specific applications.
This book is organized in two parts. Part I deals with image processing. It is
divided χη four sections, containing 32 papers. Part II, called pattern
recognition has three sections, two of which contain 11 papers covering a number
of topics in feature extraction, clustering, mapping and population
classification. The third section presents two papers on topics of interest both
in pattern recognition and in artificial intelligence.
Section I contains papers dealing with various methodological aspects of image
processing.
The first four papers consider some aspects of filtering problems.
The paper by Young et al. (p. 5) describes techniques to determine, from the image
statistics, the parameters, e.g. neighbourhood size, of Minkowski filters applied
to binary images. The robustness of the techniques is evaluated.
Biemond in his paper (p. 17) describes the similar problem of how to derive a
Kaiman filter for image restoration by identifying the degradation the image has
suffered. The results of this identification procedure are illustrated by
subsequently applying the derived Kaiman filter to images with different amounts
of noise.
The contribution by Bosman et al. (p. 3D describes two algorithms for robust
enhancement of images: The lateral inhibition algorithm (LIA), inspired by a
natural vision model and the constant volume response summation (CVRS) algorithm.
The properties of both algorithms are evaluated and compared with each other.
The paper by Good (p. 47) illustrates computer experiments, simulating the growth
of snow crystals in the free atmosphere and in the snow pack, by applying local
transformations with differing neighbourhoods and 0-1 transition rules. A striking
similarity of the configurations obtained with hexagonal plates, dendritic snow
crystals and structural elements of depth hoar is shown.
Förstner et al. (p. 57) give an overview of methods of photogrammetry. The
integration of image matching techniques into photogrammetric standard methods is
shown to be advantageous for high-precision surface measurements in e.g.
industrial objects and cartography.
Another aspect of precision of measurements in images is described in the paper by
Dorst et al. (p. 73). These authors compare six methods of estimating the length
of digitized straight line segments from their chain code. Also, aspects of
computational complexity are considered and recommendations for or against the use
of the various methods are given.
Haralick (p. 81) describes the implementation of edge and ridge detectors, based
upon his cubic facet model.
A method of edge detection in noisy images based on dynamic programming is
described and evaluated in the paper by Gerbrands et al. (p. 91). Results are
VI Preface
illustrated by applying the algorithm to cardiac scintigraphic images.
Quantitative evaluation is performed using two synthetic images in the presence of
varying amounts of noise. The performance is shown to compare favourably with that
of various parallel edge detection schemes.
Saint-Jean et al. (p. 101) explain their pretopological texture model in some
detail and show how this, when linked to a hierarchical classification method may
be of use in the analysis of cytological data.
The use of contextual information in image processing by computing (in linear
time) the joint likelihood of pixel labels and neighbouring image values, assuming
a Markov mesh model is considered in the paper by Devijver (p. 113). Examples of
image smoothing and segmentation are given to illustrate the technique and to
validate the underlying assumptions.
The paper by Hertzberger et al. (p. 125) considers the properties of a virtual
image understanding multiprocessor machine based on fifth-generation computer
concepts.
Toussaint in his paper (p. 135) reviews various problems in computational geometry
and presents solutions that are either computationally less complex or otherwise
simpler than solutions published so far. The concept of "rotating calipers" is
shown to lead to these surprising simplifications of the algorithms.
Section II contains 6 papers dealing with knowledge based or model driven image
understanding systems.
Nagy et al. (p. 1^9) describe a system in which documents are represented as a
tree structure of nested rectangular blocks. The problem of labeling the blocks
using a knowledge base is considered in view of its complexity. The proposed
solution has potential applications in document archival, transmission and
retrieval.
Interpretation of industrial scenes using knowledge represented in either a
procedural or in a declarative form is the subject of the paper by Stein et al.
(p. 161). Advantages and disadvantages of both approaches are discussed. It is
concluded that a combination of both forms of representation offers a promising
solution in this class of image interpretation problems.
Hofman et al. (p. 173) describe a system for the interpretation of sequences of
heart scintigrams, based on expert knowledge represented as an associative
network. All modules of their system are described; the module containing the
knowledge representation in considerable detail.
The dynamic modeling procedure (DMP) proposed by Tan et al. (p. 185) integrates
the properties of an unknown shape into a set of basic shapes, thus creating a set
of "suitable models". Global and local properties are used at different levels of
the procedure. Results are illustrated using numeric characters from different
typewriters.
The paper by Persoon (p. 199) describes hierarchical correlation techniques which
with VLSI implementation are no longer prohibitive. The hardware realizations are
described and illustrated on examples of industrial object location.
Another model based system is described by Dhome et al. (p. 211). Methods based
on a generalization of the Hough-transform are used to accumulate hypotheses on
the values of the parameters representing local patterns composing the objects in
a scene. An application to scenes of overlapping objects is given.
Section III contains 6 papers treating 3-D reconstruction methods.
The first of these, by Shapiro et al. (p. 221) describes techniques to estimate
the shape of three-dimensional surfaces from topographic labeling of images.
Primitives such as peaks, pits, valleys, etc. appear to obey a set of rules that
may be used to infer the shape of the surfaces of three-dimensional objects.
Analytical and experimental results are presented, using various conic surfaces as
test objects.
Determination of object pose under two imaging environments is the subject of the
paper by Stockman (p. 233). Matching of evidence obtained from the image with
model features leads to a candidate pose, which may then be verified by top-down
checking or bottom-up clustering of all candidates. It is concluded that global
image analysis is neither necessary nor desirable in the environments assumed, and
Preface vu
furthermore that it is relatively easy to use multiple views for pose
determination.
The paper by Bolle et al. (p. 243) discusses techniques for the combination of
partial information for object position estimation. By modeling an object by a
small number of patches of planes, spheres and cylinders, the correspondence
between sensed and model primitives constitutes the pieces of information which
are then integrated using a probabilistic framework. This framework is applicable
not only to position estimation, but may also be applied to entirely different
problems that can be decomposed into subproblems.
The paper by Kaminuma et al. (p. 255) describes a system for three-dimensional
reconstruction applied to sequences of microscopic images in biomedicine.
Embryogenesis of nematodes is used as an example to illustrate the capabilities of
the system.
The analysis of serial sections in histology, described in the paper by Abmayr et
al. (p. 267) is another example where 3-D reconstruction is applied.
Reconstruction of mouse brains from serial sections and the subsequent display of
the result, using various display methods is reported to open new possibilities in
histopathological work.
Three-dimensional reconstruction from projections is the subject of the paper by
Harauz et al. (p. 279). The complicating circumstance is that the angular
relationship between the various projections is unknown in the application of
macromolecular structure determination. Correspondence analysis can be used to
determine a set of eigenimages, which may then be clustered into classes,
reflecting the angular relationships.
Section IV contains 8 application oriented papers.
The first paper in this sequence, by Van Heel (p. 291), describes a procedure to
obtain the characteristic views from a set of images of macromolecules.
Techniques of alignment, averaging and correspondence analysis are outlined and
are shown to constitute a powerful sorting technique. The purpose of this
eigenimage decomposition is the 3-D reconstruction of the macromolecule. This is
described in the paper by Harauz et al. in Section III (page 279).
The paper by Mann et al. (p. 301) is concerned with the analysis of images
generated by two-dimensional gel electrophoresis in order to localize, identify
and quantify proteins. Also, methods to compare protein spots in different gel
images are described. Possible future use of an expert system to guide the
analysis is indicated.
Jordan et al. (p. 313) describe a study of intracellular movement of macrophages.
Their procedure combines manual entry of aligned image sequences with computerized
3-D reconstruction and graphical display to appreciate the lysosome movement. In
addition, quantitative analysis based on nearest neighbour techniques is
discussed. Finally, the use of a model of lysosome movement is reported to have
helped in understanding the mechanism of lysosome movement.
The paper by Harms et al. (p. 323) focuses on the use of colour in the
segmentation of tissue sections. The authors have extended their methods proven in
blood smear analysis to be applicable in histological preparations. Their method
is reported to be robust against the usual variations in color and general quality
found in routine laboratory preparations.
Artery detection in cine-angiograms is the subject of the contribution by Van
Ommeren et al. (p. 331). They describe a minimum cost path algorithm to detect the
coronary tree. The method is shown to be also applicable to retinal angiograms in
order to study the quality of the retinal vessels. In a limited study the
arteries could be traced without any human intervention.
An overview of industrial pattern recognition is given in the paper by Suetens et
al. (p. 345). All components of a visual inspection system are discussed. Also,
hardware realizations of specific functions such as, amongst others, run length
encoding, edge detection and texture analysis, are outlined.
Groen et al. (p. 363) report on a method for the analysis of schematic diagrams.
Global topology is first established, followed by probabilistic graph matching to
classify the components. Cellular logic operations are reported to be a fast tool
for the global analysis of electronical circuit diagrams and the classification is
Vlll Preface
stated to be robust against different designers and drawing types.
The paper by Parikh (p. 373) discusses the integration of low level information
and geological expertise in order to detect geological fracture patterns. The
integration process is guided by a knowledge base and applied to Landsat imagery.
The system is designed to be implemented on the Massively Parallel Processor
(MPP).
Section V is a collection of papers treating various aspects of statistical
pattern recognition.
The paper by Morgera (p. 389) examines feature selection algorithms for use in
VLSI implementation. The partial eigensystem decomposition is shown to have an
intrinsic parallel structure if the input covariance matrix is centrosymmetric.
When implemented as linear arrays of processing elements, computational complexity
is 0(N), where N is the problem dimensionality, as compared to 0(N**2) in the
sequentional approach.
The paper by Wu et al. (p. 401) considers the problem of object detection,
regardless of orientation. The method of circular harmonic function expansion is
used to generate rotation-invariant data. From these, features to be used in
classification are selected, using the Foley-Sammon transform. Two examples
supplement the mathematical exposition.
Diday et al. in their paper (p. 411) present the pyramidal representation as an
extension of hierarchical clustering. Pyramids are reported to contain more
information, are closer to the initial data and lead to overlapping classes rather
than to partitions. The definition of pyramids, their graphical representation and
their construction are outlined.
Clustering large data sets is the title of the paper by Kaufman et al. (p. 425).
A clustering program of complexity 0(N), in computation time as well as storage
requirements (N being the number of objects) is described. Sets of tens of
thousands of objects are reported to have been processed by the program,
implemented on a CDC Cyber 170/750.
The paper by Pedrycz (page 439) deals with fuzzy sets as a formalism to be used in
pattern recognition. Algorithms for classification and clustering, operating on
linguistic object descriptions are derived. Three numerical studies conclude the
paper.
Talmon in his paper (p. 449) presents a partitioning algorithm based on entropy
reduction. This non-parametric technique, when applied to a general multiclass
problem, results in a binary decision tree. At each branch point in the tree, an
optimal feature and a corresponding optimal threshold is generated by maximizing
the reduction in uncertainty of the object class-membership. In some of the
examples it is shown that the algorithm performs well in cases which are totally
unsuited for a parametric (Fisher linear discriminant) approach.
Sjostrom (p. 461) presents a projection method based on partial least squares.
Contrary to principle component mapping, this technique takes object class
membership of the objects in the training set into account in the design of the
projection transformation. The algorithm is illustrated on an example derived from
molecular biology.
An application of pattern recognition in nuclear reactor monitoring is described
in the paper by Dubuisson et al. (p. 471). Initially, only one state of the
reactor (the normal state) is known and pattern vectors can only be classified as
belonging to this class, or they are rejected. The use of a clustering algorithm
on the rejected vectors is shown to lead to the detection of other states. Also,
states evolving from one class to another may be detected, using a potential
function approach.
The paper by Bietti et al. (p. 481) describes some applications of the interactive
system for pattern analysis ISPAHAN. Applications in archaeology as well as in
physics are outlined.
Section VI contains two papers on the problem of population classification, which
has recently attracted much interest in automated cytology.
Smeulders (p. 497) describes a non-parametric technique to classify a population
on the basis of observations on its members. He introduces the "population
Preface ix
function" which must be estimated from a learning set. Testprocedures to be
applied in sequential classification are discussed.
The paper by Burger et al. (p. 509) considers the use of binary decision tree
classifiers in the classification of specimens in cytology. When linear
discriminant analysis is used, results are often degraded by the matrix pooling
operation. An interesting comparison is made between the performance of decision
trees constructed by pooling of classes (POC) and of trees constructed by
selection of classes (SOC). Experimental results demonstrate the adverse effect of
class pooling.
Section VII contains two papers on topics of interest in pattern recognition and
in artificial intelligence.
Berenstein et al. (p. 523) present a survey of consensus and evidence theory and
discuss potential research directions for combining consensus and evidence theory
techniques.
The contribution by Chandrasekaran (p. 5^7) takes the reader through a progression
of approaches to classification. Classification has been a major concern in
pattern recognition and it is an important task performed by expert systems.
Combining the opinions of experts is a problem of broad interest and considerable
difficulty.
The collection of papers in this book indicates the continuing development of
pattern recognition and image processing methodology and the strong need for such
methodology in many fields of application. It also reflects the fact that
techniques originally developed in artificial intelligence may be successfully
incorporated in pattern recognition systems.
Amsterdam , Edzard S. Gelsema
College Park, Laveen N. Kanal
ACKNOWLEDGEMENTS
We wish to thank the following Organizations, Foundations and Companies for their
financial support of the conference:
The Royal Dutch Academy of Sciences
Organization for Applied Physical Research TNO
US National Science Foundation
The Free University, Amsterdam
Philips International B.V.
KONTRON Bildanalyse GmbH
Shell Nederland B.V.
Oce-van der Grinten N.V.
Elettronica San Giorgio-Elsag
We also want to thank a number of individuals for their active help and support in
the preparatory phase of the conference.
First of all, Mr. C.E. Queiros carried out his duties as a member of the
Organizing Committee, including the less than interesting ones, with energy and
enthousiasm. The success of the conference from the organizational point of view
is for a large part due to his efforts.
As always, Professor J.H. van Bemmel was a willing source of advice and
encouragement.
The stimulating discussions with Professor E. Backer regarding the organization of
the conference are gratefully acknowledged. Also, his help as a member of the
Program Committee, as well as the help received from Professor I.T. Young have
contributed to a high-standard scientific content of the conference.
We wish to thank Dr. Warren Thompson, director of the National Science Foundation
US-Netherlands program, for his assistance.
During the conference, much work was done by the two scientific secretaries J.A.
Kors and J. van der Lei, under the skillful direction of Dr. J.L. Talmon. All
discussions were transcribed in readable form and corrected by the discussants
within hours after the closing session. Also, the editing of the discussions into
the form in which they appear in this book is largely due to the efforts of Dr.
Talmon. His help is gratefully acknowledged.
The secretariat of the conference was in the expert hands of Mrs. Ciska Kessler.
She took a heavy burden off the shoulders of the conference organisers.
Mr. H.C. den Harink supervised the operation of the technical equipment.
xii A cknowledgements
The work behind the screens, in the editorial office of the two secretaries: Mrs.
Mirella van Velzen and Mrs. Yolande Willemse is also gratefully acknowledged.
Finally, the speakers, authors of the contributions and the discussants determine
for a large part the the scientific contents of a conference. It is mainly due to
their contributions that this conference was a success. Many participants
reported that they appreciated this format of a small conference, with many
opportunities to discuss the problems and the possibilities of pattern recognition
in practice.
The editors.
PATTERN RECOGNITION IN PRACTICE II
E.S. Gelsema and L.N. Kanal (Editors)
© Elsevier Science Publishers B.V. (North-Holland), 1986 5
Choosing Filter Parameters for Non-Linear Image Filtering
Ian T. Young, Guus L. Beckers, Leo Dorst, Annelies Boerman
Department of Applied Physics
Delft University of Technology
Delft, The Netherlands
The use of non-linear image filters requires knowledge of the
image statistics so that filter parameters can be appropriately
chosen. We have developed efficient techniques for measuring the
distribution of relevant image statistics for the case of filters
based upon mathematical morphology, the liinkowski filters.
These statistics can then be used to choose appropriate values
for filter parameters. Further, we have evaluated the
performance of the measuring techniques in the presence of
image noise to determine their robustness.
Introduction
The development, implementation, and use of non-linear image filtering techniques
have been one of the major innovations in image processing in the past decade.
Filters such as the median filter (Huang, 1981), "rolling ball" filters (Sternberg,
1980), edge-preserving smoothers (Kuwahara, 1976), and Minkowski filters (Serra,
1982) have all proved their effectiveness in a wide variety of applications. Just as
in linear filtering, each of these filters has associated with it one or more filter
parameters. In the case of the median filter this might be the size of the filter
and the shape of its two-dimensional domain. A problem of critical importance
is, therefore, how are these parameters to be chosen?
In the absence of an encompassing theory fo ra given type of non-linear filter, it is
often difficult to choose the appropriate parameters for a given application. This is
equally true, of course, for linear filters. If we wish to choose the cutoff frequency
of a low-pass filter for use in the processing of, say, seismic data ,then, while
various physical theories might yield a hypothesis of where that frequency may be,
it is almost always necessary to make a series of measurements on actual seismic
signals to determine precisely where the signal spectrum "rolls-off" into the noise
spectrum. From these data the appropriate choice of the parameters of the filters
can be made.
In the research we report here we have looked at the problem of choosing the
neighborhood size - and to a certain extent shape - for the class of Minkowski
filters applied to binary images. There are numerous examples in the literature of
using the erosion filter in combination with the operation propagation (or
reconstruction) to eliminate small objects, that is, objects with a "width" less than
