163,385 research outputs found
Veni Vidi Vici, A Three-Phase Scenario For Parameter Space Analysis in Image Analysis and Visualization
Automatic analysis of the enormous sets of images is a critical task in life
sciences. This faces many challenges such as: algorithms are highly
parameterized, significant human input is intertwined, and lacking a standard
meta-visualization approach. This paper proposes an alternative iterative
approach for optimizing input parameters, saving time by minimizing the user
involvement, and allowing for understanding the workflow of algorithms and
discovering new ones. The main focus is on developing an interactive
visualization technique that enables users to analyze the relationships between
sampled input parameters and corresponding output. This technique is
implemented as a prototype called Veni Vidi Vici, or "I came, I saw, I
conquered." This strategy is inspired by the mathematical formulas of numbering
computable functions and is developed atop ImageJ, a scientific image
processing program. A case study is presented to investigate the proposed
framework. Finally, the paper explores some potential future issues in the
application of the proposed approach in parameter space analysis in
visualization
Visual Analysis of Popping in Progressive Visualization
Progressive visualization allows users to examine intermediate results while they are further refined in the background. This makes them increasingly popular when dealing with large data and computationally expensive tasks. The characteristics of how preliminary visualizations evolve over time are crucial for efficient analysis; in particular unexpected disruptive changes betweeniterations can significantly hamper the user experience. This paper proposes a visualization framework to analyze the refinement behavior of progressive visualization. We particularly focus on sudden significant changes between the iterations, which we denote as popping artifacts, in reference to undesirable visual effects in the context of level of detail representations in computergraphics. Our visualization approach conveys where in image space and when during the refinement popping artifacts occur. It allows to compare across different runs of stochastic processes, and supports parameter studies for gaining further insights and tuning the algorithms under consideration. We demonstrate the application of our framework and its effectiveness via twodiverse use cases with underlying stochastic processes: adaptive image space sampling, and the generation of grid layouts
Scale Stain: Multi-Resolution Feature Enhancement in Pathology Visualization
Digital whole-slide images of pathological tissue samples have recently
become feasible for use within routine diagnostic practice. These gigapixel
sized images enable pathologists to perform reviews using computer workstations
instead of microscopes. Existing workstations visualize scanned images by
providing a zoomable image space that reproduces the capabilities of the
microscope. This paper presents a novel visualization approach that enables
filtering of the scale-space according to color preference. The visualization
method reveals diagnostically important patterns that are otherwise not
visible. The paper demonstrates how this approach has been implemented into a
fully functional prototype that lets the user navigate the visualization
parameter space in real time. The prototype was evaluated for two common
clinical tasks with eight pathologists in a within-subjects study. The data
reveal that task efficiency increased by 15% using the prototype, with
maintained accuracy. By analyzing behavioral strategies, it was possible to
conclude that efficiency gain was caused by a reduction of the panning needed
to perform systematic search of the images. The prototype system was well
received by the pathologists who did not detect any risks that would hinder use
in clinical routine
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
Complex computational models are often designed to simulate real-world
physical phenomena in many scientific disciplines. However, these simulation
models tend to be computationally very expensive and involve a large number of
simulation input parameters which need to be analyzed and properly calibrated
before the models can be applied for real scientific studies. We propose a
visual analysis system to facilitate interactive exploratory analysis of
high-dimensional input parameter space for a complex yeast cell polarization
simulation. The proposed system can assist the computational biologists, who
designed the simulation model, to visually calibrate the input parameters by
modifying the parameter values and immediately visualizing the predicted
simulation outcome without having the need to run the original expensive
simulation for every instance. Our proposed visual analysis system is driven by
a trained neural network-based surrogate model as the backend analysis
framework. Surrogate models are widely used in the field of simulation sciences
to efficiently analyze computationally expensive simulation models. In this
work, we demonstrate the advantage of using neural networks as surrogate models
for visual analysis by incorporating some of the recent advances in the field
of uncertainty quantification, interpretability and explainability of neural
network-based models. We utilize the trained network to perform interactive
parameter sensitivity analysis of the original simulation at multiple
levels-of-detail as well as recommend optimal parameter configurations using
the activation maximization framework of neural networks. We also facilitate
detail analysis of the trained network to extract useful insights about the
simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic
Visual parameter optimisation for biomedical image processing
Background: Biomedical image processing methods require users to optimise input parameters to ensure high quality
output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple
input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships
between input and output.
Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by
integrating input and output, and by supporting exploration of their relationships. We discuss its application to a
colour deconvolution technique for stained histology images and show how it enabled a domain expert to
identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify
deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying
assumption about the algorithm.
Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs
in biomedical image processing that is not supported by previous analysis software. The analysis supported by our
method is not feasible with conventional trial-and-error approaches
On the Optimization of Visualizations of Complex Phenomena
The problem of perceptually optimizing complex visualizations is a difficult one, involving perceptual as well as aesthetic issues. In our experience, controlled experiments are quite limited in their ability to uncover interrelationships among visualization parameters, and thus may not be the most useful way to develop rules-of-thumb or theory to guide the production of high-quality visualizations. In this paper, we propose a new experimental approach to optimizing visualization quality that integrates some of the strong points of controlled experiments with methods more suited to investigating complex highly-coupled phenomena. We use human-in-the-loop experiments to search through visualization parameter space, generating large databases of rated visualization solutions. This is followed by data mining to extract results such as exemplar visualizations, guidelines for producing visualizations, and hypotheses about strategies leading to strong visualizations. The approach can easily address both perceptual and aesthetic concerns, and can handle complex parameter interactions. We suggest a genetic algorithm as a valuable way of guiding the human-in-the-loop search through visualization parameter space. We describe our methods for using clustering, histogramming, principal component analysis, and neural networks for data mining. The experimental approach is illustrated with a study of the problem of optimal texturing for viewing layered surfaces so that both surfaces are maximally observable
Finding faint HI structure in and around galaxies: scraping the barrel
Soon to be operational HI survey instruments such as APERTIF and ASKAP will
produce large datasets. These surveys will provide information about the HI in
and around hundreds of galaxies with a typical signal-to-noise ratio of
10 in the inner regions and 1 in the outer regions. In addition, such
surveys will make it possible to probe faint HI structures, typically located
in the vicinity of galaxies, such as extra-planar-gas, tails and filaments.
These structures are crucial for understanding galaxy evolution, particularly
when they are studied in relation to the local environment. Our aim is to find
optimized kernels for the discovery of faint and morphologically complex HI
structures. Therefore, using HI data from a variety of galaxies, we explore
state-of-the-art filtering algorithms. We show that the intensity-driven
gradient filter, due to its adaptive characteristics, is the optimal choice. In
fact, this filter requires only minimal tuning of the input parameters to
enhance the signal-to-noise ratio of faint components. In addition, it does not
degrade the resolution of the high signal-to-noise component of a source. The
filtering process must be fast and be embedded in an interactive visualization
tool in order to support fast inspection of a large number of sources. To
achieve such interactive exploration, we implemented a multi-core CPU (OpenMP)
and a GPU (OpenGL) version of this filter in a 3D visualization environment
().Comment: 17 pages, 9 figures, 4 tables. Astronomy and Computing, accepte
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