Visualizations are widely used when working with family and genealogical structures, both to navigate through the generations and to provide overview information about the family as a whole. Our research investigates the concept of "marriage" in the complex and polygamous familial structures of Mormon society in mid-1800s Nauvoo, IL, including several definitions of marital and relational ties. We have found current visualizations to be insufficient in fully expressing this complexity. We present visualizations based on chord and flow diagrams to capture the locality and cohesiveness of larger and more complex family units and encapsulate familial dynamics into the nodes of their overall lineage. Each family unit is portrayed as a chord diagram adapted to display intra-familial relationships with a left-to-right generational flow and chords indicating relationships between participants. Zooming out, we depict the overall lineage as a modified flow diagram with the family units as nodes, connected with others based on the participants; each hyper-edge links an individual's family of birth to her adult marriage.<br/> Our implementation has yielded evocative and provocative visualizations–preserving locality of family unit members, an overall temporal order on their display, and distinguishability of relational types–by which scholars can investigate these complex social structure.

Display systems suitable for virtual reality applications can prove useful for a variety of domains. The emergence of low-cost head-mounted displays reinvigorated the area of virtual reality significantly. However, there are still applications where full-scale CAVE-type display systems are better suited. Moreover, the cost of most CAVE-type display systems is typically rather high, thereby making it difficult to justify in a research setting. This article aims at providing a design of less costly display technology combined with inexpensive input devices that implements a virtual environment paradigm suitable for such full-scale visualization and simulation tasks. The focus is on cost-effective display technology that does not break a researchers budget. The software framework utilizing these displays combines different visualization and graphics packages to create an easy-to-use software environment that can run readily on this display. A user study was performed to evaluate the display technology and its usefulness for virtual reality tasks using an accepted measure: presence. It was found that the display technology is capable of delivering a virtual environment in which the user feels fully immersed. © 2017 Society for Imaging Science and Technology.

Advancement in wearable devices has allowed users to easily capture and monitor their physical activity data. There is a growing interest in using the data captured by these devices for selfmanagement of chronic diseases. People with chronic conditions are encouraged by their clinicians to maintain a non-sedentary lifestyle. However, people tend to be forgetful when it comes to non-sedentary behaviours [26, 41].</a> We have developed "FitViz-Ad", a non-intrusive reminder to encourage people with Rheumatoid Arthritis to maintain a non-sedentary lifestyle. We conducted a study with fourteen healthy individuals to evaluate the feasibility of FitViz-Ad. We did not find any significant increase in participants' non-sedentary behaviour. However, the findings suggest design implications for developing reminders which encourage non-sedentary behaviour and accommodate different lifestyles.

Phase space tessellation techniques for N–body dark matter simulations yield density fields of very high quality. However, due to the vast amount of elements and self-intersections in the resulting tetrahedral meshes, interactive visualization methods for this approach so far either employed simplified versions of the volume rendering integral or suffered from rendering artifacts. This paper presents a volume rendering approach for phase space tessellations, that combines state-of-the-art order–independent transparency methods to manage the extreme depth complexity of this mesh type. We propose several performance optimizations, including a view–dependent multiresolution representation of the data and a tile–based rendering strategy, to enable high image quality at interactive frame rates for this complex data structure and demonstrate the advantages of our approach for different types of dark matter simulations.

The Fels method is a well-known method for assessing the skeletal maturity from hand-wrist X-ray images. This method estimates the skeletal maturity age by manually grading multiple indicators for different hand-wrist bones. Due to the large number of indicators that need to be measured, this is a time-consuming task, especially with large databases of X-ray images. Furthermore, it can be a very subjective task that depends on the observer. Therefore, the need for automation of this process is in high demand. In this study, we have proposed a semi-automatic method to grade a sub-set of Fels indicators. This method is composed of four main steps of pre-processing, ROI extraction, segmentation, and Fels indicator grading. The most challenging step of the algorithm is to segment different bones in the Fels regions of interest (wrist, Finger I, III and V ROIs) which have been done using local Otsu thresholding and active contour filtering. The result of segmentation is evaluated visually on a subset of Fels study data set.

We introduce a web-based visual comparison approach for the systematic exploration of dynamic activation networks across biological datasets. Understanding the dynamics of such networks in the context of demographic factors like age is a fundamental problem in computational systems biology and neuroscience. We design visual encodings for the dynamic and community characteristics of these temporal networks. Our multi-scale approach blends nested mosaic matrices that capture temporal characteristics of the data, spatial views of the network data, Kiviat diagrams and mirror glyphs that detail the temporal behavior and community assignment of specific nodes. A top design specifically targeted at pairwise visual comparison further supports the comparative analysis of multiple dataset activations. We demonstrate the effectiveness of this approach through a case study on mouse brain network data. Domain expert feedback indicates this approach can help identify trends and anomalies in the data. © 2017 Society for Imaging Science and Technology.

Mutual Information (MI) is emerging as a very strong metric for image registration purposes in the literature. It has many applications from remote sensing to medical image registration. From this wide range of use of MI, images are mostly expressed in different numbers of bits (high dynamic range) especially in medical and satellite imaging. In such cases, contrast enhancement becomes inevitable before MI-based image registration since all the images should be in the same intensity range. The change in intensities in images will directly affect MI metric. Contrast enhancement methods also have a significant effect on the registration performance due to MI metric and this problem is not sufficiently addressed in the literature. In this paper, the effect of the outstanding contrast enhancement methods is examined on image registration performance. For this purpose, high dynamic range satellite images were used and Monte Carlo tests were performed. They are tried to be aligned with MI and constrained optimization by linear approximations (COBYLA) optimization algorithm. Consequently, it is found that contrast enhancement methods have an effect on MI-based image registration. It is concluded that Laplacian of Gaussian unsharp blending masks (LoGUnsarp), adaptive histogram equalization (AHE) and contrast limited adaptive histogram equalization (CLAHE) methods have better registration performance. They can be preferred in such registration purposes.

In this paper, we present CNVis, a web-based visual analytics tool for exploring data from multiple related academic conferences, mainly consisting of the papers presented at the conferences and participants who bookmark these papers. Our goal is to investigate the bookmarking relationships within a single conference and interpret various conference relationships and trends via effective visualization, comparison, and recommendation. This is achieved through the design and development of three coordinated views (the bookmark, topic, and keyword views) for user interaction and exploration. We demonstrate the effectiveness of CNVis using real-world data from three related conferences over a period of five years, followed by an ad-hoc expert evaluation of the tool. Finally, we discuss the extension of this work and the generalizability of CNVis for other applications.

Automatic generation of data visualizations allows to quickly deploy data visualizations. In visual analytics, the combination of automatic and human analysis increases the effort necessary to achieve similar effects substantially. Where automatic visualization only needs to map the data, in visual analytics the whole data preparation and processing pipeline has to be considered. The user is interested in representations reflecting certain interpretations of the data, for example the idea that different groups represent different clusters in the data. In this paper, we prove that an information-driven automatic design of visual analytics pipelines is feasible. To this end, we prove that the ability of an analysis system to derive and visualize data supporting inquired information is decidable – at least for real-world applications. Having overcome this major obstacle, we outline a general algorithm scheme that can be implemented on a wide range of data and information models.