Martin Kampel

3D MURALE: a multimedia system for archaeology

This paper introduces the 3D Measurement and Virtual Reconstruction of Ancient Lost Worlds of Europe system (3D MURALE). It consists of a set of tools for recording, reconstructing, encoding, visualising and database searching/querying that operate on buildings, building parts, statues, statue parts, pottery, stratigraphy, terrain geometry and texture and material texture. The tools are loosely linked together by a common database on which they all have the facility to store and access data. The paper describes the overall architecture of the 3D MURALE system and then briefly describes the functionality of the tools provided by the project. The paper compares the multimedia studio architecture adopted in this project with other multimedia studio architectures.

Introducing the use of depth data for fall detection

Current emergency systems for elderly contain at least one sensor (button or accelerometer), which has to be worn or pressed in case of emergency. If elderly fall and loose their consciousness, they are not able to press the button anymore. Therefore, autonomous systems to detect falls without wearing any devices are needed. This paper presents three different non-invasive technologies: the use of audio, 2D sensors (cameras) and introduces a new technology for fall detection: the Kinect as 3D depth sensor. Our fall detection algorithms using the Kinect are evaluated on 72 video sequences, containing 40 falls and 32 activities of daily living. The evaluation results are compared with State-of-the-Art approaches using 2D sensors or microphones.

Visual Surveillance for Aircraft Activity Monitoring

This paper presents a visual surveillance system for the automatic scene interpretation of airport aprons. The system comprises two modules - scene tracking and scene understanding. The scene tracking module, comprising a bottom-up methodology, and the scene understanding module, comprising a video event representation and recognition scheme, have been demonstrated to be a valid approach for apron monitoring

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.

Evaluation of Motion Segmentation Quality for Aircraft Activity Surveillance

Recent interest has been shown in performance evaluation of visual surveillance systems. The main purpose of performance evaluation on computer vision systems is the statistical testing and tuning in order to improve algorithms reliability and robustness. In this paper we investigate the use of empirical discrepancy metrics for quantitative analysis of motion segmentation algorithms. We are concerned with the case of visual surveillance on an airports apron, that is the area where aircrafts are parked and serviced by specialized ground support vehicles. Robust detection of individuals and vehicles is of major concern for the purpose of tracking objects and understanding the scene. In this paper, different discrepancy metrics for motion segmentation evaluation are illustrated and used to assess the performance of three motion segmentors on video sequences of an airports apron.

Model-based registration of front- and backviews of rotationally symmetric objects

This paper shows an algorithm that prealigns the front- and the backviews of rotationally symmetric objects for the registration of the two 3D-surfaces without using corresponding points. The geometric alignment of the two 3D surfaces is then performed by using a modified ICP (iterative closest point) algorithm, which needs an initial estimate of the relative pose. The method proposed uses the axis of rotation of fragments to bring two range images into alignment. We are developing a classification system for archaeological fragments based on their profile, which is the cross-section of the fragment in the direction of the rotational axis of symmetry. Hence, the correct registration of the front- and backview are important. We demonstrate the method and give results on synthetic and real data.

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.

On 3D mosaicing of rotationally symmetric ceramic fragments

A major obstacle to the wider use of 3D object reconstruction and modeling is the extent of manual intervention needed. Such interventions are currently massive and exist throughout every phase of a 3D reconstruction project: collection of images, image management, establishment of sensor position and image orientation, extracting the geometric detail describing an object, merging geometric, texture and semantic data. This work aims to develop a solution for automated documentation of archaeological pottery, which also leads to a more complete 3D model out of multiple fragments. Generally the 3D reconstruction of arbitrary objects from their fragments can be regarded as a 3D puzzle. In order to solve it we identified the following main tasks: 3D data acquisition, orientation of the object, classification of the object and reconstruction. We demonstrate the method and give results on synthetic and real data.

Profile-based Pottery Reconstruction

A major obstacle to the broader use of 3D object reconstruction and modeling is the extent of manual intervention needed. Such interventions are currently extensive and exist throughout every phase of a 3D reconstruction project: collection of images, image management, establishment of sensor position and image orientation, extracting the geometric information describing an object, and merging geometric, texture and semantic data. We present a fully automated approach to pottery reconstruction based on the fragment profile, which is the cross-section of the fragment in the direction of the rotational axis of symmetry. We demonstrate the method and give results on synthetic and real data.

Interest Point Based Tracking

This paper deals with a novel method for object tracking. In the first step interest points are detected and feature descriptors around them are calculated. Sets of known points are created, allowing tracking based on point matching. The set representation is updated online at every tracking step. Our method uses one-shot learning with the first frame, so no offline and no supervised learning is required. Following an object recognition based approach there is no need for a background model or motion model, allowing tracking of abrupt motion and with non-stationary cameras. We compare our method to Mean Shift and Tracking via Online Boosting, showing the benefits of our approach.