115,477 research outputs found
Visualization of spectral images
Spectral image sensors provide images with a large number of contiguous spectral channels per pixel. Visualization of these huge data sets is not a straightforward issue. There are three principal ways in which spectral data can be presented; as spectra, as image and in feature space. This paper describes several visualization methods and their suitability in the different steps in the research cycle. Combinations of the three presentation methods and dynamic interaction between them, adds significant to the usability. Examples of some software implementations are given. Also the application of volume visualization methods to display spectral images is shown to be valuabl
Understanding deep features with computer-generated imagery
We introduce an approach for analyzing the variation of features generated by
convolutional neural networks (CNNs) with respect to scene factors that occur
in natural images. Such factors may include object style, 3D viewpoint, color,
and scene lighting configuration. Our approach analyzes CNN feature responses
corresponding to different scene factors by controlling for them via rendering
using a large database of 3D CAD models. The rendered images are presented to a
trained CNN and responses for different layers are studied with respect to the
input scene factors. We perform a decomposition of the responses based on
knowledge of the input scene factors and analyze the resulting components. In
particular, we quantify their relative importance in the CNN responses and
visualize them using principal component analysis. We show qualitative and
quantitative results of our study on three CNNs trained on large image
datasets: AlexNet, Places, and Oxford VGG. We observe important differences
across the networks and CNN layers for different scene factors and object
categories. Finally, we demonstrate that our analysis based on
computer-generated imagery translates to the network representation of natural
images
Multiscale correlative tomography: an investigation of creep cavitation in 316 stainless steel
Creep cavitation in an ex-service nuclear steam header Type 316 stainless steel sample is investigated through a multiscale tomography workflow spanning eight orders of magnitude, combining X-ray computed tomography (CT), plasma focused ion beam (FIB) scanning electron microscope (SEM) imaging and scanning transmission electron microscope (STEM) tomography. Guided by microscale X-ray CT, nanoscale X-ray CT is used to investigate the size and morphology of cavities at a triple point of grain boundaries. In order to understand the factors affecting the extent of cavitation, the orientation and crystallographic misorientation of each boundary is characterised using electron backscatter diffraction (EBSD). Additionally, in order to better understand boundary phase growth, the chemistry of a single boundary and its associated secondary phase precipitates is probed through STEM energy dispersive X-ray (EDX) tomography. The difference in cavitation of the three grain boundaries investigated suggests that the orientation of grain boundaries with respect to the direction of principal stress is important in the promotion of cavity formation
Robust Geometry Estimation using the Generalized Voronoi Covariance Measure
The Voronoi Covariance Measure of a compact set K of R^d is a tensor-valued
measure that encodes geometric information on K and which is known to be
resilient to Hausdorff noise but sensitive to outliers. In this article, we
generalize this notion to any distance-like function delta and define the
delta-VCM. We show that the delta-VCM is resilient to Hausdorff noise and to
outliers, thus providing a tool to estimate robustly normals from a point cloud
approximation. We present experiments showing the robustness of our approach
for normal and curvature estimation and sharp feature detection
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