A spreadsheet interface for visualization exploration

Jankun-Kelly, T. J.; Ma, Kwan‐Liu

doi:10.1109/visual.2000.885678

Cited by 34 publications

(22 citation statements)

References 16 publications

(13 reference statements)

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“…Alternative approaches to facilitate parameter explorations have resulted in systems like Design Galleries [25], Image Graphs [23], spreadsheet-like exploration interfaces [19], and VisTrails [37]. None of these approaches is utilizing an optimization function nor is the user able to see a comprehensive overview of which places of the parameter space have been "looked at" and which have not.…”

Section: Related Workmentioning

confidence: 99%

Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration

Torsney-Weir

Saad

Möller

et al. 2011

IEEE Trans. Visual. Comput. Graphics

105

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Abstract-In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the "goodness" of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans.Index Terms-Parameter exploration, Image segmentation, Gaussian Process Model. MOTIVATIONFor visual analysis image data often need to be segmented. Segmentation refers to the process of partitioning the image into multiple segments, i.e. sets of pixels or voxels, that form contiguous and semantically meaningful regions. If each of these regions is marked by a unique identifier, image segmentation simply means labelling of pixels or voxels. In biomedical imaging, where images are acquired using some kind of tomography or microscopy, segmented regions might correspond to anatomical structures in the case of nonfunctional imaging, and to regions with specific physiological activity in the case of functional imaging.In recent years a variety of semi-and fully automatic techniques have been developed to address the segmentation problem [32]. However, even the current state-of-the-art approaches fall short of providing a "silver bullet" for image segmentation. This has several reasons. One reason is that given some image, the segmentation problem is not well defined; in fact it depends on the application which regions are semantically meaningful. Another reason is that due to different image degradation factors such as low signal-to-noise ratio, imaging artifacts, partial volume effects and shape variability, different kinds of a priori knowledge need to be included. Additionally, the majority of the existing segmentation methods rely on and are sensitive to setting a number of parameters. For example, most of the algorithms contain weighting parameters between multiple competing image-driven or prior-driven cost terms in an attempt to mimic the cognitive capabilities of expert users (e.g. radiologists for medical images).A good parameter setting is usually found by a manual trial and error procedure. Th...

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Section: Related Workmentioning

confidence: 99%

Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration

Torsney-Weir

Saad

Möller

et al. 2011

IEEE Trans. Visual. Comput. Graphics

105

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“…Jankun-Kelly and Ma developed a technique based on a spreadsheet metaphor [10], again to investigate the influence of rendering parameters on computer graphics scenes. Pairs of parameters define a two-dimensional table with their domains, respectively, defining the coordinates of the x-and y-axes.…”

Section: Related Workmentioning

confidence: 99%

Visualization of Parameter Space for Image Analysis

Pretorius

Bray

Carpenter

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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Image analysis algorithms are often highly parameterized and much human input is needed to optimize parameter settings. This incurs a time cost of up to several days. We analyze and characterize the conventional parameter optimization process for image analysis and formulate user requirements. With this as input, we propose a change in paradigm by optimizing parameters based on parameter sampling and interactive visual exploration. To save time and reduce memory load, users are only involved in the first step - initialization of sampling - and the last step - visual analysis of output. This helps users to more thoroughly explore the parameter space and produce higher quality results. We describe a custom sampling plug-in we developed for CellProfiler - a popular biomedical image analysis framework. Our main focus is the development of an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. We implemented this in a prototype called Paramorama. It provides users with a visual overview of parameters and their sampled values. User-defined areas of interest are presented in a structured way that includes image-based output and a novel layout algorithm. To find optimal parameter settings, users can tag high- and low-quality results to refine their search. We include two case studies to illustrate the utility of this approach.

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“…Ma [8] presents the Image Graph design which enables sharing of visualizations and the processes to create them, and allows construction of new visualization from existing ones. Jankun-Kelly et al [4,5] introduce a spreadsheet-like interface for volume visualization; through this interface, the user can search for ideal visualizations by exploring in the visualization parameter space enumerating different transfer functions, views, etc. Lu and Ebert [7] present a method to generate volume visualizations by emulating example illustrations.…”

Section: Related Workmentioning

confidence: 99%

An Interface Design for Future Cloud-Based Visualization Services

Tanahashi

Chen

Marchesin

et al. 2010

2010 IEEE Second International Conference on Cloud Computing Technology and Science

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The pervasive concept of cloud computing suggests that visualization, which is both data and computing intensive, is a perfect cloud computing application. This paper presents a sketch of an interface design for an online visualization service. To make such a service attractive to a wider audience, its user interface must be simple and easy to use for both casual and expert users. We envision an interface that supports visualization processes mainly directed by browsing and assessing existing visualizations in terms of images and videos will be very appealing to, in particular, casual users. That is, the aim is to maximize the utilization of the rich visualization data on the web. Without losing generality, we consider volume data visualization applications for our interface design. We also discuss issues in organizing online visualization data, and constructing and managing a rendering cloud.

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A spreadsheet interface for visualization exploration

Cited by 34 publications

References 16 publications

Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration

Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration

Visualization of Parameter Space for Image Analysis

An Interface Design for Future Cloud-Based Visualization Services

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