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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-5/W2, 2013
XXIV International CIPA Symposium, 2 — 6 September 2013, Strasbourg, France
THE ILAC-PROJECT: SUPPORTING ANCIENT COIN CLASSIFICATION BY MEANS
OF IMAGE ANALYSIS
Albert Kavelar”, Sebastian Zambanini*, Martin Kampel*, Klaus Vondrovec"* and Kathrin Siegl**
"Computer Vision Lab,
Vienna University of Technology
Favoritenstr. 9/183-2, 1040 Vienna, AT
{kavelar, zamba, kampel } @caa.tuwien.ac.at
http://caa.tuwien.ac.at/cvl/research/ilac/
**Coin Cabinet,
Museum of Fine Arts
Burgring 5, 1010 Vienna, AT
{klaus.vondrovec, kathrin.siegl } @khm.at
KEY WORDS: ancient coins, computer vision, numismatics, optical character recognition, image matching
ABSTRACT:
This paper presents the ILAC project, which aims at the development of an automated image-based classification system for ancient
Roman Republican coins. The benefits of such a system are manifold: operating at the suture between computer vision and numismatics,
ILAC can reduce the day-to-day workload of numismatists by assisting them in classification tasks and providing a preselection of
suitable coin classes. This is especially helpful for large coin hoard findings comprising several thousands of coins. Furthermore, this
system could be implemented in an online platform for hobby numismatists, allowing them to access background information about
their coin collection by simply uploading a photo of obverse and reverse for the coin of interest. ILAC explores different computer
vision techniques and their combinations for the use of image-based coin recognition. Some of these methods, such as image matching,
use the entire coin image in the classification process, while symbol or legend recognition exploit certain characteristics of the coin
imagery. An overview of the methods explored so far and the respective experiments is given as well as an outlook on the next steps of
the project.
1 INTRODUCTION
ILAC stands for Image-based cLassification of Ancient Coins and
is an interdisciplinary project that links the field of Computer
Vision with Numismatics and aims at generating a selection of
image-based classification methods for ancient Roman Repub-
lican coins. The competence in the field of Computer Vision is
provided by the Computer Vision Lab of the Vienna University of
Technology, whereas the Department of Coins and Medals at the
Kunsthistorisches Museum Wien (KHM, Museum of Fine Arts,
Vienna) contributes the numismatic know-how. The benefit is
mutual: Computer Vision aids numismatists in the task of clas-
sifying coins, whereas the field of Numismatics provides a large
number of new and challenging data for fundamental research in
Computer Vision. While the project’s primary focus lies on ba-
sic research in the field of Computer Vision, the long-run goal is
to create an application which assists numismatists in their every-
day work. Thus, the project bridges the gap between fundamental
scientific and application-oriented research.
Despite the existence of classification systems for modern coins,
no fully-automated image-based classification system for ancient
Roman Republican coins has yet been researched successfully.
This results from various fundamental differences between an-
cient coins and their modern counterparts: Modern coins of the
same class look identical because they are machine-made and un-
dergo quality assurance while ancient coins were struck manu-
ally. For this reason and due to wear from use and environmental
influences, such as chemicals in the soil, every ancient coin is
unique. Thus, ancient coins show a high intra-class and a low
inter-class variability (see Fig. 1), which facilitates the recogni-
tion of a specific coin specimen but impedes its classification.
373
In the course of this project, various techniques and concepts
of Computer Vision are evaluated for the application of image-
based coin classification. The sub-fields investigated reach from
content-based image retrieval over symbol recognition to opti-
cal character recognition. The project, originally set for three
years, has already reached its final year. Besides primarily deal-
ing with the development of best-practices for coin image acqui-
sition and the creation of a large-scale image database compris-
ing over 4100 Roman Republican coins, a registration-based coin
classification technique and an initial prototype of a coin legend
recognition system were developed in the first year.
In the second year, we concentrated on the research of illumination-
invariant local image features that are robust against highlights
and shadows introduced by the metallic relief surface of ancient
coins, which can change drastically depending on the lights an-
gle of incidence. The proposed image feature is based on Gabor
filters and outperforms state-of-the-art image descriptors in sce-
narios of varying illumination conditions (Zambanini and Kam-
pel, 2013b). Furthermore, the refinement of the legend recogni-
tion algorithm based on local image descriptors and the adoption
of methods first introduced in the field of scene text recognition
were pursued in the second year. The legend recognition uses
SIFT features for the description of the individual characters and
combines them to meaningful words of a given lexicon via picto-
rial structures.
The goal for the final year is to enrich the method-mix by re-
searching a coin image recognition technique, to further improve
and evaluate the methods researched so far and to ultimately com-
bine all methods to a fully-fledged coin recognition framework,
which allows to perform an image-based classification of ancient
Roman Republican coins.
