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Astronomical Data Analysis Software and Systems IV
ASP Conference Series, Vol. 77, 1995
R. A. Shaw, H. E. Payne, and J. J. E. Hayes, eds.
Object Detection Using Multi­Resolution Analysis
F. Murtagh 1
ST­ECF, ESO, Karl­Schwarzschild­Str. 2, D­85748 Garching
W. Zeilinger
Department of Astronomy, University of Vienna, T¨urkenschanzstr. 17,
A­1180 Vienna
J.­L. Starck
CEA, DSM/DAPNIA, F­91191 Gif­sur­Yvette Cedex
A. Bijaoui
OCA, BP 229, F­06304 Nice Cedex 4
Abstract. What we look for in an image is scale­dependent. Multi­
resolution allows for image analysis in terms of different scales. Scale­
space filtering, quadtrees, pyramid representations, and the wavelet trans­
form have all been used to yield multi­resolution views of an image. One
approach to the demarcation of significant structure, at different reso­
lutions, is to determine statistically significant parts of each scale. A
multi­resolution support data structure may be built up in this way. We
can go further and incorporate a priori knowledge into the construction
of this multi­resolution support. Mathematical morphology offers a con­
venient framework for removing detector artifacts, objects which are too
small in extent to be of interest, etc. This approach is used for the
detection (and subsequent analysis) of globular cluster systems around
elliptical galaxies, and for image characterization and object trawling in
large image databases.
1. Introduction
Content­based image retrieval can be tackled by using text annotations which
characterize image content. Such an approach has been prototyped 1 for Hubble
Space Telescope (HST ) WF/PC­1 data. In this paper we describe the use of
multi­resolution analysis for finding and making an inventory of objects in an
image. For the multi­resolution transform, we used the pyramidal median image
transform: see Starck, Murtagh, & Louys (1995) for further details.
1 Affiliated with the Astrophysics Division, Space Sciences Department, ESA
1 http://ecf.hq.eso.org/ fmurtagh/hst­navigate.html
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2
The use of Minkowski morphological operators allows prior knowledge re­
lating to the objects of interest to be introduced. The structuring element is
related to the morphology of the objects of interest in the image. The choice
adopted is point­symmetric; and its size is related to potentially relevant objects.
Image dilations expand the object in a locally adaptive manner. The dilated ob­
ject area may help in providing extra faint or background pixels later when the
object is characterized using ellipticity, moments, or other properties. Closings,
or erosions with a suitable structuring element followed by dilations, may be
used to remove linear features that are not usually of interest in astronomical
imagery (e.g., extended cosmic ray impacts, bleeding and charge­overflow effects
in a CCD detector, and intersections of mosaiced images).
2. Globular Clusters Surrounding Elliptical Galaxies
Earlier work by Meurs et al. (1994) aimed at finding faint, edge­on galaxies in
WF/PC images. For each object found, properties such as the number of pixels
in the object, peak­to­minimum intensity difference, a coefficient characterizing
the azimuthal profile, and the ellipticity of the principal axis, were used to allow
discrimination between potentially relevant objects on the one hand, and faint
stars or detector faults on the other. This work is currently being extended with
the study of globular cluster systems. NGC 4636 was discussed by Kissler et
al. (1993), and characterized as a rich globular cluster system in a normal ellip­
tical galaxy. An extracted 512 \Theta 512 sub­image from an ESO New Technology
Telescope (NTT) image is shown in Figure 1. A multi­resolution support of this
image was obtained. This multi­resolution support image with values 0, 2, 4,
and 8 represents, in one image, the four resolution levels examined. Figure 2
shows the result of transforming the support image in Figure 1 to a boolean
image: any pixel with value greater than 2 was assigned a value of 1, followed
by two openings. The object ``islands'' are labeled, and an associated report file
produced principally with Gaussian profile fits in X and in Y. This information
can be used to discriminate between objects of interest and those which are not.
3. Object Detection in Large Image Databases
The applicability of this approach to object detection in large image collections
has been investigated. Rather than using original WF/PC data, the associated
preview images provide two advantages. Firstly they are available on­line. Sec­
ondly they are smaller in size: typically about 1 MB per image when compressed,
compared to 10.5 MB in original form. On the negative side, the compression
scheme used in these preview images produces artifacts, and in particular the
image texture may be considerably affected. Mosaicing the WF/PC quadrants
also leaves the intersections bare.
A selection of WF/PC images was chosen with dense stellar fields, sparse
faint object fields, and filamentary structure. These images were subjected to the
following operations to allow for the great differences in the image properties,
due to varying exposure lengths, the different nature of objects viewed, and
presence of detector artifacts:

3
400 1198
950
1748
Figure 1. Part of ESO NTT image of NGC 4636.
799
1
799
1 799
1
799
Figure 2. Booleanized multi­resolution support before and after 2
closings.
1. Multi­resolution support construction.
2. Level 4 in the multi­resolution support corresponds to large gradient local
peaks---if it and level 3 accounted for more than 20% of all pixels, then
level 4 alone was retained; if levels 4 and 3 together accounted for less than
20% of the image's pixels, then they were combined into a single boolean
image. These rules were employed to specify what could be considered as
significant objects in a given image.

4
3. A set of up to 3 openings with point­symmetric structuring elements were
applied to remove remaining detector artifacts and cosmic ray hits.
4. Finally the contiguous regions were labeled and elementary properties were
determined (object extent, defined by numbers of pixels; peak­to­minimum
intensity within the object area, etc.).
This approach works well. Some remaining problems include the following:
closely located objects are gathered into contiguous object ``islands'' in the final
boolean image; and the presence of faint filamentary structure in the image in
not explicitly handled when using structuring elements and a multi­resolution
transform kernel which presuppose small, point­symmetric objects.
4. Conclusion
In this work, we ask ourselves whether it is feasible to consider automated image
characterization through object detection and description. Traditional object
inventory packages include FOCAS and MIDAS/INVENTORY, and are semi­
interactive. Can a multi­resolution approach improve generality of treatment,
and accuracy of results? Such established object inventory packages have been
limited to relative peaks in flux intensity and/or intensity gradient. The multi­
resolution transform incorporates these, and adds other potentially valuable im­
age properties. Included among these properties are: object resolution scale
(indirectly linked to gradient information); automatically ignoring the image
background (through differencing the images which make up the multi­resolution
transform); and facilitating incorporation of user­specified image processing rules
using, for example, Minkowski image operators.
References
Kissler, M., Richtler, T., Held, E. V., Grebel, E. K., Wagner, S., & Cappaccioli,
M. 1993, ESO Messenger, 32
Meurs, E. J. A., Murtagh, F., & Adorf, H.­M. 1994, IAU General Assembly
Starck, J.­L., Murtagh, F., & Bijaoui, A. 1995, this volume, p. ??
Starck, J.­L., Murtagh, F., & Louys, M. 1995, this volume, p. ??