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The ability to track moving people is a key aspect of autonomous robot systems in real-world environments. Whilst for many tasks knowing the approximate positions of people may be sufficient, the ability to identify unique people is needed to accurately count people in the real world. To accomplish the people counting task, a robust system for people detection, tracking and identification is needed.
We explore the potential of stereoscopic 3D (S3D) vision in offering distinct gameplay using an S3D-specific game called Deepress3D. Our game utilizes established S3D design principles for optimizing GUI design, visual comfort and game mechanics which rely on depth perception in time-pressured spatial conflicts. The game collects detailed S3D player metrics and allows players to choose between different, evenly matched strategies. We conducted a between subjects study comparing S3D and monoscopic versions of Deepress3D that examined player behavior and performance and measured user-reported data on presence, simulator sickness, and game experience.
The work being described in this paper is the result of a cooperation project between the Institute of Visual Computing at the Bonn-Rhein-Sieg University of Applied Sciences, Germany and the Laboratory of Biomedical Engineering at the Federal University of Uberlândia, Brazil. The aim of the project is the development of a virtual environment based training simulator which enables for better and faster learning the control of upper limb prostheses. The focus of the paper is the description of the technical setup since learning tutorials still need to be developed as well as a comprehensive evaluation still needs to be carried out.
This article describes an approach to rapidly prototype the parameters of a Java application run on the IBM J9 Virtual Machine in order to improve its performance. It works by analyzing VM output and searching for behavioral patterns. These patterns are matched against a list of known patterns for which rules exist that specify how to adapt the VM to a given application. Adapting the application is done by adding parameters and changing existing ones. The process is fully automated and carried out by a toolkit. The toolkit iteratively cycles through multiple possible parameter sets, benchmarks them and proposes the best alternative to the user. The user can, without any prior knowledge about the Java application or the VM improve the performance of the deployed application and quickly cycle through a multitude of different settings to benchmark them. When tested with the representative benchmarks, improvements of up to 150% were achieved.
A cost-efficient alternative to outside-in tracking systems for pointing interaction with large displays is to equip the pointing device with a camera, whose images are matched to display content. This work presents the Dynamic Marker Camera Tracking (DMCT) framework for display-based camera tracking. It accounts for typical display characteristics and uses dynamic on-screen markers overlaid to the display content that follow the camera. An example marker implementation and a tracking recovery method are presented. DMCT can measure pointing locations with sub-millimeter precision in large tracking volumes and computes 6-DoF camera poses for 3D interaction. 60 Hz update rate and 24 ms latency were achieved. DMCT's main limitation is the visible marker interfering with display content. In pointing effciency, the prototype is comparable to an OptiTrack system.
Software repository data, for example in issue tracking systems, include natural language text and technical information, which includes anything from log files via code snippets to stack traces. However, data mining is often only interested in one of the two types e.g. in natural language text when looking at text mining. Regardless of which type is being investigated, any techniques used have to deal with noise caused by fragments of the other type i.e. methods interested in natural language have to deal with technical fragments and vice versa. This paper proposes an approach to classify unstructured data, e.g. development documents, into natural language text and technical information using a mixture of text heuristics and agglomerative hierarchical clustering. The approach was evaluated using 225 manually annotated text passages from developer emails and issue tracker data. Using white space tokenization as a basis, the overall precision of the approach is 0.84 and the recall is 0.85.