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UncertaintyAware Exploration of Continuous Parameter Spaces Using Multivariate Prediction
"... Systems projecting a continuous ndimensional parameter space to a continuous mdimensional target space play an important role in science and engineering. If evaluating the system is expensive, however, an analysis is often limited to a small number of sample points. The main contribution of this p ..."
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Cited by 37 (5 self)
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Systems projecting a continuous ndimensional parameter space to a continuous mdimensional target space play an important role in science and engineering. If evaluating the system is expensive, however, an analysis is often limited to a small number of sample points. The main contribution of this paper is an interactive approach to enable a continuous analysis of a sampled parameter space with respect to multiple target values. We employ methods from statistical learning to predict results in realtime at any userdefined point and its neighborhood. In particular, we describe techniques to guide the user to potentially interesting parameter regions, and we visualize the inherent uncertainty of predictions in 2D scatterplots and parallel coordinates. An evaluation describes a realworld scenario in the application context of car engine design and reports feedback of domain experts. The results indicate that our approach is suitable to accelerate a local sensitivity analysis of multiple target dimensions, and to determine a sufficient local sampling density for interesting parameter regions. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation—Line and curve generation 1.
Tuner: Principled parameter finding for image segmentation algorithms using visual response surface exploration
 IEEE Transactions on Visualization and Computer Graphics
, 2011
"... Abstract—In this paper we address the difficult problem of parameterfinding in image segmentation. We replace a tedious manual process that is often based on guesswork and luck by a principled approach that systematically explores the parameter space. Our core idea is the following twostage techn ..."
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Cited by 22 (6 self)
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Abstract—In this paper we address the difficult problem of parameterfinding in image segmentation. We replace a tedious manual process that is often based on guesswork and luck by a principled approach that systematically explores the parameter space. Our core idea is the following twostage 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 groundtruth 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. 1
Adaptive Interactive MultiResolution Computational Steering for Complex Engineering Systems
"... Computational steering integrates modeling, computation, data analysis, visualization, and data input components of a simulation. Since the simulation space is, in general, very large and continuous, selecting discrete simulation points that can reasonably present the whole simulation space is diffi ..."
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Computational steering integrates modeling, computation, data analysis, visualization, and data input components of a simulation. Since the simulation space is, in general, very large and continuous, selecting discrete simulation points that can reasonably present the whole simulation space is difficult. We need to interpolate the “missing” values and cover a continuous region of interest in the simulation space. We describe an approach that, in an iterative manner, allows a domain expert to interactively select data points (design of experiments), approximate the values in a continuous region of the simulation space (regression) and automatically find the “best ” points in that continuous region based on the specified constraints and objectives (optimization), using the regression and aggregated data. Once the objectives are found, data points in the neighborhood of the objective are generated by the simulation tool thus providing denser coverage of the regions of interest. 1. Introduction and Related