Maia Zaharieva

Recognizing Ancient Coins Based on Local Features

Numismatics deals with various historical aspects of the phenomenon money. Fundamental part of a numismatists work is the identification and classification of coins according to standard reference books. The recognition of ancient coins is a highly complex task that requires years of experience in the entire field of numismatics. To date, no optical recognition system for ancient coins has been investigated successfully. In this paper, we present an extension and combination of local image descriptors relevant for ancient coin recognition. Interest points are detected and their appearance is described by local descriptors. Coin recognition is based on the selection of similar images based on feature matching. Experiments are presented for a database containing ancient coin images demonstrating the feasibility of our approach.

Image based recognition of ancient coins

Illegal trade and theft of coins appears to be a major part of the illegal antiques market. Image based recognition of coins could substantially contribute to fight against it. Central component in the permanent identification and traceability of coins is the underlying classification and identification technology. However, currently available algorithms focus basically on the recognition of modern coins. To date, no optical recognition system for ancient coins has been researched successfully. In this paper, we give an overview on recent research for coin classification and we show if existing approaches can be extended from modern coins to ancient coins. Results of the algorithms implemented are presented for three different coins databases with more then 10.000 coins.

Automated social event detection in large photo collections

The detection of a specific social event requires for high semantic understanding in the interpretation of particular event characteristics such as its type and location. In many cases, photos capturing different events at the same (or highly similar) locations can hardly be distinguished by each other. Available metadata can provide assistance where there is no expert knowledge at hand. However, metadata often lack completeness and reliability. In this paper, we explore the feasibility of a fully automated approach for the detection of specific social events. In comparison to related approaches, we do not incorporate query-specific processing and we perform no manual adaptation of the input query. The resulting approach is applicable to arbitrary event types.

MobiLearn: An Open Approach for Structuring Content for Mobile Learning Environments

Mobile devices are becoming more and more important in the context of e-learning. This requires appropriate models for structuring and delivering content to be used on various devices. Different technical characteristics of devices as well as different needs of learners require specific approaches. In this paper we propose a model for structuring content that allows rendering for different devices like Notebooks, PDAs, and Smartphones as well as presentation of the content in different levels of details according to didactic concepts like case study, definition, example, interaction, motivation, directive. This approach allows adaptation of content (device, granularity of content, content selection based on didactic concepts) at run time to specific needs in a particular learning situation. The approach realized in the joint MobiLearn project of several universities in Austria shows high acceptance by students during an initial pilot application.

Identification of ancient coins based on fusion of shape and local features

We present a vision-based approach to ancient coins’ identification. The approach is a two-stage procedure. In the first stage an invariant shape description of the coin edge is computed and matching based on shape is performed. The second stage uses preselection by the first stage in order to refine the matching using local descriptors. Results for different descriptors and coin sides are combined using naive Bayesian fusion. Identification rates on a comprehensive data set of 2400 images of ancient coins are on the order of magnitude of 99%.

A novel trajectory clustering approach for motion segmentation

We propose a novel clustering scheme for spatio-temporal segmentation of sparse motion fields obtained from feature tracking. The approach allows for the segmentation of meaningful motion components in a scene, such as short- and long-term motion of single objects, groups of objects and camera motion. The method has been developed within a project on the analysis of low-quality archive films. We qualitatively and quantitatively evaluate the performance and the robustness of the approach. Results show, that our method successfully segments the motion components even in particularly noisy sequences.

Image-Based Retrieval and Identification of Ancient Coins

Reliable object identification is an essential task in the process of recognizing and tracing stolen cultural heritage. We investigate the feasibility of using computer-aided identification of ancient coins to search for a given coin on the Internet or in a digital repository. Because a coins shape is a unique feature, we first apply a shape descriptor to capture its characteristics. Then, we use local features to describe the die information. The approach presented here shows promise for reliably identifying objects in the area of cultural heritage.

On ancient coin classification

Illegal trade and theft of coins appears to be a major part of the illegal antiques market. Image based recognition of coins could substantially contribute to fight against it. Central component in the permanent identification and traceability of coins is the underlying classification and identification technology. The first step of a computer aided system is the segmentation of the coin in the image. Next, a feature extraction process measures the coin in order to describe the coin unambiguously. In this paper, we focus on the segmentation task, followed by a comparison of features relevant for coin classification. Results of the algorithms implemented are presented for an image database of ancient coins.

Scene segmentation in artistic archive documentaries

Scene segmentation is a crucial task in the structural analysis of film. State-of-the-art scene segmentation algorithms usually target fiction films (e.g. Hollywood films). Documentaries (especially artistic archive documentaries) follow different montage rules than fiction films and consequently require specialized approaches for scene segmentation. We propose a scene segmentation algorithm targeted at artistic archive documentaries. We evaluate the performance of our technique with archive documentaries and contemporary movies and obtain satisfactory results in both domains.

Numismatic Object Identification Using Fusion of Shape and Local Descriptors

Reliable object identification is an essential task in the process of recognition and traceability of stolen cultural heritage. Existing approaches for object recognition focus mainly on object classification. However, they are not sufficient to identify a given object among hundreds of objects of the same class. In this paper, we investigate the feasibility of computer aided identification of ancient coins. Since the shape of a coin is a very unique feature, we first apply a shape descriptor to capture its characteristics. In the next step, local features are used to describe the die information. We present experiments on a data set of 2400 images of ancient coins. The evaluation results show that our approach is competitive. Moreover, it indicates some outstanding features that show great promise for reliable object identification in the area of cultural heritage.