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data- and model-driven gaze control for an active-vision system

IEEE TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2001年第12期23卷 1415-1429页

作者： Backer, G Mertsching, B Bollmann, M Univ Hamburg Dept Comp Sci IMA Lab D-22527 Hamburg Germany Ericsson Eurolab Deutschland GmbH PPDC Packet Switching Syst Syst Management D-31135 Hildesheim Germany

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.

关键词： visual attention gaze control visual exploration active vision

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PolyVis: Cross-Device Framework for Collaborative visual data analysis

PolyVis: Cross-Device Framework for Collaborative Visual Dat...

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IEEE International conference on Systems, Man and Cybernetics (SMC)

作者： Alsaiari, Abeer Johnson, Andrew Nishimoto, Arthur Univ Illinois Elect Visualizat Lab Chicago IL 60607 USA

ISBN: (纸本)9781728145693

Multi device environments present new opportunities for collaborative visual data analysis and sense making by utilizing each device's strengths and capabilities. However, one of the associated challenges with visual data analysis 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 visual data exploration. 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.

关键词： visualization

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Matching visual Saliency to Confidence in Plots of Uncertain data

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2010年第6期16卷 980-989页

作者： Feng, David Kwock, Lester Lee, Yueh Taylor, Russell M., ii Univ N Carolina Chapel Hill NC 27515 USA UNC Hosp Dept Radiol Chapel Hill NC USA

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.

关键词： Uncertainty visualization brushing scatter plots parallel coordinates multivariate data

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Fast training of Self Organizing Maps for the visual exploration of molecular compounds

Fast training of Self Organizing Maps for the visual explora...

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International Joint conference on Neural Networks

作者： Fiannaca, Antonino Di Fatta, Giuseppe Rizzo, Riccardo Urso, Alfonso Gaglio, Salvatore CNR ICAR I-00185 Rome Italy Univ Reading Sch Syst Engn Reading Berks England Univ Palermo DINFO Palermo Italy

ISBN: (纸本)9781424413799

visual exploration 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 data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration 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 visual exploration 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.

关键词： Self organizing maps

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Multiscale visualization: A Structured Literature analysis

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2022年第12期28卷 4918-4929页

作者： Cakmak, Eren Jaeckle, Dominik Schreck, Tobias Keim, Daniel A. Fuchs, Johannes Univ Konstanz Dept Comp & Informat Sci D-78547 Constance Germany Graz Univ Technol Inst Comp Graph & Knowledge Visualisat A-8010 Graz Austria

Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visual exploration 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.

关键词： data visualization Navigation visualization Taxonomy Encoding Molecular biology Libraries Multiscale visualization multiscale navigation multiscale exploration literature analysis taxonomy survey

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Mobility Graphs: visual analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2016年第1期22卷 11-20页

作者： von Landesberger, Tatiana Brodkorb, Felix Roskosch, Philipp Andrienko, Natalia Andrienko, Gennady Kerren, Andreas Tech Univ Darmstadt Darmstadt Germany Fraunhofer IAIS Bonn Germany City Univ London London EC1V 0HB England Linnaeus Univ Vaxjo Sweden

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 data exploration 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.

关键词： visual analytics movement data networks graphs temporal aggregation spatial aggregation flows clustering

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A System for visual exploration of Caution Spots from Vehicle Recorder data 10

A System for Visual Exploration of Caution Spots from Vehicl...

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10th IEEE conference on visual Analytics Science and Technology (VAST)

作者： Itoh, Masahiko Yokoyama, Daisaku Toyoda, Masashi Kitsuregawa, Masaru Univ Tokyo Tokyo 1138654 Japan Natl Inst Informat & Commun Technol Koganei Tokyo 1848795 Japan Natl Inst Informat Tokyo Japan

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.

关键词： Transportation industry recorders Accident rate Property values exploration Automobiles Braking vehicle Accidents visualization Toxic Plants Allergy Spot Spots acceleration

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Interactive visual data exploration with subjective feedback: an information-theoretic approach

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data MINING AND KNOWLEDGE DISCOVERY 2020年第1期34卷 21-49页

作者： Puolamaki, Kai Oikarinen, Emilia Kang, Bo Lijffijt, Jefrey De Bie, Tijl Univ Helsinki Dept Comp Sci Helsinki Finland Univ Ghent Dept Elect & Informat Syst IDLab Ghent Belgium

visual exploration of high-dimensional real-valued datasets is a fundamental task in exploratory data analysis (EDA). Existing projection methods for data visualization 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.

关键词： Exploratory data analysis Dimensionality reduction Information theory Subjective interestingness Maximum entropy distribution

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River-water Quality exploration 13

River-water Quality Exploration

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13th IEEE conference on visual Analytics Science and Technology (VAST)

作者： Beham, Michael Splechtna, Rainer Gracanin, Denis Ginina, Elena Matkovic, Kresimir VRVis Res Ctr Vienna Austria Virginia Tech Blacksburg VA USA

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 data exploration and analysis based on the newly introduced data model, interaction, and views.

关键词： Human-centered computing visualization visualization Techniques

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visualizing visualizations: User interfaces for managing and exploring scientific visualization data (vol 20, pg 16, 2000)

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2000年第6期20卷 11-11页

作者： Ma, KL Univ Calif Davis Dept Comp Sci 2063 Engn II1 Shields Ave Davis CA 95616 USA.

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 data visualization 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 data exploration should be shared and reused. In particular, for types of data visualization 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 data exploration process and results can be incorporated into and become a part of the user interface of a data exploration 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

关键词： User interfaces Peer to peer computing data visualization Transfer functions Rendering (computer graphics) Magnetic resonance imaging Foot Two dimensional displays Combustion data engineering

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