With the rapid development in graphics hardware and volume rendering techniques, many volumetric datasets can now be rendered in real time on a standard PC equipped with a commodity graphics board. However, the effectiveness of the results, especially direct volume rendered images, is difficult to validate and users may not be aware of ambiguous or even misleading information in the results. This limits the applications of volume visualization. In this paper, we introduce four quantitative effectiveness measures: distinguishability, contour clarity, edge consistency, and depth coherence measures, which target different effectiveness issues for direct volume rendered images. Based on the measures, we develop a visualization system with automatic effectiveness assessment, providing users with instant feedback on the effectiveness of the results. The case study and user evaluation have demonstrated the high potential of our system.

Radio frequency identification (RFID) is a powerful automatic remote identification technique that has wide applications. To facilitate RFID deployment, an RFID benchmarking instrument called aGate has been invented to identify the strengths and weaknesses of different RFID technologies in various environments. However, the data acquired by aGate are usually complex time varying multidimensional 3D volumetric data, which are extremely challenging for engineers to analyze. In this paper, we introduce a set of visualization techniques, namely, parallel coordinate plots, orientation plots, a visual history mechanism, and a 3D spatial viewer, to help RFID engineers analyze benchmark data visually and intuitively. With the techniques, we further introduce two workflow procedures (a visual optimization procedure for finding the optimum reader antenna configuration and a visual analysis procedure for comparing the performance and identifying the flaws of RFID devices) for the RFID benchmarking, with focus on the performance analysis of the aGate system. The usefulness and usability of the system are demonstrated in the user evaluation.

Volume exploration is an important issue in scientific visualization. Research on volume exploration has been focused on revealing hidden structures in volumetric data. While the information of individual structures or features is useful in practice, spatial relations between structures are also important in many applications and can provide further insights into the data. In this paper, we systematically study the extraction, representation, exploration, and visualization of spatial relations in volumetric data and propose a novel relation-aware visualization pipeline for volume exploration. In our pipeline, various relations in the volume are first defined and measured using region connection calculus (RCC) and then represented using a graph interface called relation graph. With RCC and the relation graph, relation query and interactive exploration can be conducted in a comprehensive and intuitive way. The visualization process is further assisted with relation-revealing viewpoint selection and color and opacity enhancement. We also introduce a quality assessment scheme which evaluates the perception of spatial relations in the rendered images. Experiments on various datasets demonstrate the practical use of our system in exploratory visualization.