Models of visual attention provide a general approach to control the activities of active vision systems. We will introduce a new model of attentional control that differs in important aspects from conventional ones. ...
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Models of visual attention provide a general approach to control the activities of active vision systems. We will introduce a new model of attentional control that differs in important aspects from conventional ones. We divide the selection into two stages, which is more suitable for the system as well as explaining different phenomena found in natural visual attention, such as the dispute between early and late selection. The proposed model is especially designed for use in dynamic scenes. Our approach alms at modeling as much of a general active vision system as possible and designing clean interfaces for the integration of the remaining specific aspects needed in order to solve specific problems.
Multi device environments present new opportunities for collaborative visualdataanalysis and sense making by utilizing each device's strengths and capabilities. However, one of the associated challenges with vis...
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ISBN:
(纸本)9781728145693
Multi device environments present new opportunities for collaborative visualdataanalysis and sense making by utilizing each device's strengths and capabilities. However, one of the associated challenges with visualdataanalysis in multi device environments is the sharing of visual components across devices. We present a framework developed on top of SAGE2 platform for cross-device collaborative visualdataexploration. As part of our framework, we contribute the concept of rapid development and assembling of visualizations that can span multiple devices of different modalities. It provides the users with an environment for visualization compositions that delegate the rendering to the target device, allowing them to augment their large display workspace with portable devices for further exploration territories. Facilitated by its intuitive visualization composition pipeline, users with no programming skills such as data analysts can enhance their analytical scope with no coding barriers. We describe the framework, its implementation with a use case, and the rationale behind its design.
Conveying data uncertainty in visualizations is crucial for preventing viewers from drawing conclusions based on untrustworthy data points. This paper proposes a methodology for efficiently generating density plots of...
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Conveying data uncertainty in visualizations is crucial for preventing viewers from drawing conclusions based on untrustworthy data points. This paper proposes a methodology for efficiently generating density plots of uncertain multivariate data sets that draws viewers to preattentively identify values of high certainty while not calling attention to uncertain values. We demonstrate how to augment scatter plots and parallel coordinates plots to incorporate statistically modeled uncertainty and show how to integrate them with existing multivariate analysis techniques, including outlier detection and interactive brushing. Computing high quality density plots can be expensive for large data sets, so we also describe a probabilistic plotting technique that summarizes the data without requiring explicit density plot computation. These techniques have been useful for identifying brain tumors in multivariate magnetic resonance spectroscopy data and we describe how to extend them to visualize ensemble data sets.
visualexploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen's Self Organizing Map (SOM) is a widely used tool for visualization of ...
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ISBN:
(纸本)9781424413799
visualexploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen's Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a dataanalysis framework for the generation of similarity maps. Such maps provide an effective tool for the visualexploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds;the generated maps allow a visualexploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions.
Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visualexploration of hierarchical genome structures in molecular biology. However, cre...
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Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visualexploration of hierarchical genome structures in molecular biology. However, creating such multiscale visualizations remains challenging due to the plethora of existing work and the expression ambiguity in visualization research. Up to today, there has been little work to compare and categorize multiscale visualizations to understand their design practices. In this article, we present a structured literature analysis to provide an overview of common design practices in multiscale visualization research. We systematically reviewed and categorized 122 published journal or conference articles between 1995 and 2020. We organized the reviewed articles in a taxonomy that reveals common design factors. Researchers and practitioners can use our taxonomy to explore existing work to create new multiscale navigation and visualization techniques. Based on the reviewed articles, we examine research trends and highlight open research challenges.
Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well a...
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Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time moments) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods. We propose a visual analytics methodology that solves these issues by combined spatial and temporal simplifications. We have developed a graph-based method, called Mobility Graphs, which reveals movement patterns that were occluded in flow maps. Our method enables the visual representation of the spatio-temporal variation of movements for long time series of spatial situations originally containing a large number of intersecting flows. The interactive system supports dataexploration from various perspectives and at various levels of detail by interactive setting of clustering parameters. The feasibility our approach was tested on aggregated mobility data derived from a set of geolocated Twitter posts within the Greater London city area and mobile phone call data records in Abidjan, Ivory Coast. We could show that Mobility Graphs support the identification of regular daily and weekly movement patterns of resident population.
It is vital for the transportation industry, which performs most of its work by automobiles, to reduce its accident rate. This paper proposes a 3D visual interaction method for exploring caution areas from large-scale...
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ISBN:
(纸本)9781467397834
It is vital for the transportation industry, which performs most of its work by automobiles, to reduce its accident rate. This paper proposes a 3D visual interaction method for exploring caution areas from large-scale vehicle recorder data. Our method provides (i) a flexible filtering interface for driving operations such as braking or handling operations by various combinations of their attribute values such as velocity and acceleration, and (ii) a 3D visual environment for spatio-temporal exploration of caution areas. The proposed method was able to extract caution areas where some accidents have actually occurred or that are on very narrow roads with bad visibility by using real data given by one of the biggest transportation companies in Japan.
visualexploration of high-dimensional real-valued datasets is a fundamental task in exploratory dataanalysis (EDA). Existing projection methods for datavisualization use predefined criteria to choose the representa...
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visualexploration of high-dimensional real-valued datasets is a fundamental task in exploratory dataanalysis (EDA). Existing projection methods for datavisualization use predefined criteria to choose the representation of data. There is a lack of methods that (i) use information on what the user has learned from the data and (ii) show patterns that she does not know yet. We construct a theoretical model where identified patterns can be input as knowledge to the system. The knowledge syntax here is intuitive, such as "this set of points forms a cluster", and requires no knowledge of maths. This background knowledge is used to find a maximum entropy distribution of the data, after which the user is provided with data projections for which the data and the maximum entropy distribution differ the most, hence showing the user aspects of data that are maximally informative given the background knowledge. We study the computational performance of our model and present use cases on synthetic and real data. We find that the model allows the user to learn information efficiently from various data sources and works sufficiently fast in practice. In addition, we provide an open source EDA demonstrator system implementing our model with tailored interactive visualizations. We conclude that the information theoretic approach to EDA where patterns observed by a user are formalized as constraints provides a principled, intuitive, and efficient basis for constructing an EDA system.
We describe our analysis of VAST Challenge 2018 Mini-Challenge 2 data set using a collection of visualization tools. We used the tools for better user interaction and to introduce new views in support of visual analyt...
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ISBN:
(纸本)9781538668610
We describe our analysis of VAST Challenge 2018 Mini-Challenge 2 data set using a collection of visualization tools. We used the tools for better user interaction and to introduce new views in support of visual analytics. We answer some of the challenge questions and plan further research on dataexploration and analysis based on the newly introduced data model, interaction, and views.
The process of scientific visualization is inherently iterative. A good visualization comes from experimenting with visualization, rendering, and viewing parameters to bring out the most relevant information in the da...
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The process of scientific visualization is inherently iterative. A good visualization comes from experimenting with visualization, rendering, and viewing parameters to bring out the most relevant information in the data. A good datavisualization system thus lets scientists interactively explore the parameter space intuitively. The more efficient the system, the fewer the number of iterations needed for parameter selection. Over the past 10 years, significant efforts have gone into advancing visualization technology (such as real-time volume rendering and immersive environments), but little into coherently representing the process and results (images and insights) of visualization. This information about the dataexploration should be shared and reused. In particular, for types of datavisualization with a high cost of producing images and less than obvious relationship between the rendering parameters and the image produced, a visual representation of the exploration process can make the process more efficient and effective. This visual representation of dataexploration process and results can be incorporated into and become a part of the user interface of a dataexploration system. That is, we need to go beyond the traditional graphical user interface (GUI) design by coupling it with a mechanism that helps users keep track of their visualization experience, use it to generate new visualizations, and share it with others. Doing so can reduce the cost of visualization, particularly for routine analysis of large-scale data sets
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