285 research outputs found
Visualization of uncertain scalar data fields using color scales and perceptually adapted noise
Session: VisualizationInternational audienceWe present a new method to visualize uncertain scalar data fields by combining color scale visualization techniques with animated, perceptually adapted Perlin noise. The parameters of the Perlin noise are controlled by the uncertainty information to produce animated patterns showing local data value and quality. In order to precisely control the perception of the noise patterns, we perform a psychophysical evaluation of contrast sensitivity thresholds for a set of Perlin noise stimuli. We validate and extend this evaluation using an existing computational model. This allows us to predict the perception of the uncertainty noise patterns for arbitrary choices of parameters. We demonstrate and discuss the efficiency and the benefits of our method with various settings, color maps and data sets
Learning to Predict Image-based Rendering Artifacts with Respect to a Hidden Reference Image
Image metrics predict the perceived per-pixel difference between a reference
image and its degraded (e. g., re-rendered) version. In several important
applications, the reference image is not available and image metrics cannot be
applied. We devise a neural network architecture and training procedure that
allows predicting the MSE, SSIM or VGG16 image difference from the distorted
image alone while the reference is not observed. This is enabled by two
insights: The first is to inject sufficiently many un-distorted natural image
patches, which can be found in arbitrary amounts and are known to have no
perceivable difference to themselves. This avoids false positives. The second
is to balance the learning, where it is carefully made sure that all image
errors are equally likely, avoiding false negatives. Surprisingly, we observe,
that the resulting no-reference metric, subjectively, can even perform better
than the reference-based one, as it had to become robust against
mis-alignments. We evaluate the effectiveness of our approach in an image-based
rendering context, both quantitatively and qualitatively. Finally, we
demonstrate two applications which reduce light field capture time and provide
guidance for interactive depth adjustment.Comment: 13 pages, 11 figure
Towards Data-Driven Large Scale Scientific Visualization and Exploration
Technological advances have enabled us to acquire extremely large
datasets but it remains a challenge to store, process, and extract
information from them. This dissertation builds upon recent advances
in machine learning, visualization, and user interactions to
facilitate exploration of large-scale scientific datasets. First, we
use data-driven approaches to computationally identify regions of
interest in the datasets. Second, we use visual presentation for
effective user comprehension. Third, we provide interactions for
human users to integrate domain knowledge and semantic information
into this exploration process.
Our research shows how to extract, visualize, and explore informative
regions on very large 2D landscape images, 3D volumetric datasets,
high-dimensional volumetric mouse brain datasets with thousands of
spatially-mapped gene expression profiles, and geospatial trajectories
that evolve over time. The contribution of this dissertation include:
(1) We introduce a sliding-window saliency model that discovers
regions of user interest in very large images; (2) We develop visual
segmentation of intensity-gradient histograms to identify meaningful
components from volumetric datasets; (3) We extract boundary surfaces
from a wealth of volumetric gene expression mouse brain profiles to
personalize the reference brain atlas; (4) We show how to efficiently
cluster geospatial trajectories by mapping each sequence of locations
to a high-dimensional point with the kernel distance framework.
We aim to discover patterns, relationships, and anomalies that would
lead to new scientific, engineering, and medical advances. This work
represents one of the first steps toward better visual understanding
of large-scale scientific data by combining machine learning and human
intelligence
Laboratoire de physiologie de la perception et de l’action
Responsable : Sidney Wiener Le laboratoire de Physiologie de la perception et de l’action (LPPA) poursuit ses recherches avec des approches multidisciplinaires dans le champ des neurosciences intégratives chez l’homme et des modèles expérimentaux animaux. Nos thèmes de recherche sont centrés sur les bases neurales des fonctions cognitives, en mettant l’accent sur les interactions entre perception et action et bases neurales de la mémoire. Recherche Développement perceptif et intégration inter..
A Fast and Scalable System to Visualize Contour Gradient from Spatio-temporal Data
Changes in geological processes that span over the years may often go unnoticed due to their inherent noise and variability. Natural phenomena such as riverbank erosion, and climate change in general, is invisible to humans unless appropriate measures are taken to analyze the underlying data. Visualization helps geological sciences to generate scientific insights into such long-term geological events. Commonly used approaches such as side-by-side contour plots and spaghetti plots do not provide a clear idea about the historical spatial trends.
To overcome this challenge, we propose an image-gradient based approach called ContourDiff. ContourDiff overlays gradient vector over contour plots to analyze the trends of change across spatial regions and temporal domain. Our approach first aggregates for each location, its value differences from the neighboring points over the temporal domain, and then creates a vector field representing the prominent changes. Finally, it overlays the vectors (differential trends) along the contour paths, revealing the differential trends that the contour lines (isolines) experienced over time.
We designed an interface, where users can interact with the generated visualization to reveal changes and trends in geospatial data. We evaluated our system using real-life datasets, consisting of millions of data points, where the visualizations were generated in less than a minute in a single-threaded execution. We show the potential of the system in detecting subtle changes from almost identical images, describe implementation challenges, speed-up techniques, and scope for improvements. Our experimental results reveal that ContourDiff can reliably visualize the differential trends, and provide a new way to explore the change pattern in spatiotemporal data. The expert evaluation of our system using real-life WRF (Weather Research and Forecasting) model output reveals the potential of our technique to generate useful insights on the spatio-temporal trends of geospatial variables
Saliency Prediction in the Data Visualization Design Process
L'abstract è presente nell'allegato / the abstract is in the attachmen
Simulation-based Planning of Machine Vision Inspection Systems with an Application to Laser Triangulation
Nowadays, vision systems play a central role in industrial inspection. The experts typically choose the configuration of measurements in such systems empirically. For complex inspections, however, automatic inspection planning is essential. This book proposes a simulation-based approach towards inspection planning by contributing to all components of this problem: simulation, evaluation, and optimization. As an application, inspection of a complex cylinder head by laser triangulation is studied
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