The Leap Motion Controller is a new device for hand gesture controlled user interfaces with declared sub-millimeter accuracy. However, up to this point its capabilities in real environments have not been analyzed. Therefore, this paper presents a first study of a Leap Motion Controller. The main focus of attention is on the evaluation of the accuracy and repeatability. For an appropriate evaluation, a novel experimental setup was developed making use of an industrial robot with a reference pen allowing a position accuracy of 0.2 mm. Thereby, a deviation between a desired 3D position and the average measured positions below 0.2mmhas been obtained for static setups and of 1.2mmfor dynamic setups. Using the conclusion of this analysis can improve the development of applications for the Leap Motion controller in the field of Human-Computer Interaction.

The three-dimensional cuneiform script is one of the oldest known writing systems and a central object of research in Ancient Near Eastern Studies and Hittitology. An important step towards the understanding of the cuneiform script is the provision of opportunities and tools for joint analysis. This paper presents an approach that contributes to this challenge: a collaborative compatible web-based scientific exploration and analysis of 3D scanned cuneiform fragments. The WebGL -based concept incorporates methods for compressed web-based content delivery of large 3D datasets and high quality visualization. To maximize accessibility and to promote acceptance of 3D techniques in the field of Hittitology, the introduced concept is integrated into the Hethitologie-Portal Mainz, an established leading online research resource in the field of Hittitology, which until now exclusively included 2D content. The paper shows that increasing the availability of 3D scanned archaeological data through a web-based interface can provide significant scientific value while at the same time finding a trade-off between copyright induced restrictions and scientific usability.

Automated content-based search for arbitrary cuneiform signs in photographic reproductions is a challenging task in the analysis of ancient documents, a central component of which is a reliable cuneiform sign classification. We present an illumination-based approach to generate synthetic training data for cuneiform sign classification via deep neural networks to overcome common issues with the transferability of machine learning training results. Starting from an analysis of the negative impact of illumination variations in the processed cuneiform data, we employ an illumination augmentation to two-dimensional (2D) training data generated from annotated 3D datasets. We demonstrate that our method is able to overcome the high visual variance of most digitized 2D cuneiform reproductions and achieve an illumination invariant generalization. The effectiveness of our approach is evaluated by its successful application to several subsets of a cuneiform script dataset with an originally poor transferability of mutual training results. Furthermore, we show that a sufficient sampling of the illumination space mostly removes the necessity to match the training data to specific target illumination conditions. The practical applicability of our approach is validated by applying it to a larger dataset, raising the overall classification accuracy by 4 percentage points to 90%, resulting in a classification error reduction of 28.5% when compared to results without the proposed data augmentation.

Along with the ongoing success of virtual/augmented reality (VR/AR) and human–machine interaction (HMI) in the professional and consumer markets, new compatible and inexpensive hand tracking devices are required. One of the contenders in this market is the Leap Motion Controller 2 (LMC2), successor to the popular Leap Motion Controller (LMC1), which has been widely used for scientific hand-tracking applications since its introduction in 2013. To quantify ten years of advances, this study compares both controllers using quantitative tracking metrics and characterizes the interaction space above the sensor. A robot-actuated 3D-printed hand and a motion-capture system provide controlled movements and external reference data. In the central tracking volume, the LMC2 achieves improved performance, reducing palm-position error from 7.9–9.8 mm (LMC1) to 5.2–5.3 mm (LMC2) and lowering positional variability from 1.3–2.2 mm to 0.4–0.8 mm. Dynamic tests confirm stable tracking for both devices. For boundary experiments, the LMC2 maintains continuous detection at distances up to 666 mm, compared to 250–275 mm (LMC1), and detects hands entering the field of view from distances up to 646 mm. Both devices show reduced accuracy toward the edges of the tracking volume. Overall, the results provide a grounded characterization of LMC2 performance in its newly emphasized VR/AR-relevant interaction spaces, while the metrics support cross-comparison with earlier LMC1-based studies and transfer to related application scenarios.

The cuneiform script constitutes one of the earliest systems of writing and is realized by wedge-shaped marks on clay tablets. A tremendous number of cuneiform tablets have already been discovered and are incrementally digitalized and made available to automated processing. As reading cuneiform script is still a manual task, we address the real-world application of recognizing cuneiform signs by two graph based methods with complementary runtime characteristics. We present a graph model for cuneiform signs together with a tailored distance measure based on the concept of the graph edit distance. We propose efficient heuristics for its computation and demonstrate its effectiveness in classification tasks experimentally. To this end, the distance measure is used to implement a nearest neighbor classifier leading to a high computational cost for the prediction phase with increasing training set size. In order to overcome this issue, we propose to use CNNs adapted to graphs as an alternative approach shifting the computational cost to the training phase. We demonstrate the practicability of both approaches in an extensive experimental comparison regarding runtime and prediction accuracy. Although currently available annotated real-world data is still limited, we obtain a high accuracy using CNNs, in particular, when the training set is enriched by augmented examples.

The formal analysis of ancient sculptures plays a crucial role in understanding historical contexts, cultures, and societies. In-depth archaeological studies often rely on mostly manual, time-consuming and comprehensive visual analysis conducted by specialists in the field. This work explores the integration of deep learning methods, based on neural network architectures, for segmenting facial components and classifying portraits of the first four Roman emperors of the Julio-Claudian dynasty, with the aim of supporting archaeologists in their work. The segmentation is evaluated objectively using the Intersection over Union metric (IoU), with scores around 0.85, in combination with qualitative visual analysis via image masks. For the classification task, F1-scores of approximately 0.78 are achieved for emperor recognition, and 0.79 for portrait type classification. Furthermore, highlighting the decisive regions used by the network for classification offers a new perspective on the importance of facial regions compared to the traditionally considered hair patterns.