4,091 research outputs found
Extending stochastic resonance for neuron models to general Levy noise
A recent paper by Patel and Kosko (2008) demonstrated stochastic resonance (SR) for general feedback continuous and spiking neuron models using additive Levy noise constrained to have finite second moments. In this brief, we drop this constraint and show that their result extends to general Levy noise models. We achieve this by showing that �¿large jump�¿ discontinuities in the noise can be controlled so as to allow the stochastic model to tend to a deterministic one as the noise dissipates to zero. SR then follows by a �¿forbidden intervals�¿ theorem as in Patel and Kosko's paper
Artifact-Based Rendering: Harnessing Natural and Traditional Visual Media for More Expressive and Engaging 3D Visualizations
We introduce Artifact-Based Rendering (ABR), a framework of tools,
algorithms, and processes that makes it possible to produce real, data-driven
3D scientific visualizations with a visual language derived entirely from
colors, lines, textures, and forms created using traditional physical media or
found in nature. A theory and process for ABR is presented to address three
current needs: (i) designing better visualizations by making it possible for
non-programmers to rapidly design and critique many alternative data-to-visual
mappings; (ii) expanding the visual vocabulary used in scientific
visualizations to depict increasingly complex multivariate data; (iii) bringing
a more engaging, natural, and human-relatable handcrafted aesthetic to data
visualization. New tools and algorithms to support ABR include front-end
applets for constructing artifact-based colormaps, optimizing 3D scanned meshes
for use in data visualization, and synthesizing textures from artifacts. These
are complemented by an interactive rendering engine with custom algorithms and
interfaces that demonstrate multiple new visual styles for depicting point,
line, surface, and volume data. A within-the-research-team design study
provides early evidence of the shift in visualization design processes that ABR
is believed to enable when compared to traditional scientific visualization
systems. Qualitative user feedback on applications to climate science and brain
imaging support the utility of ABR for scientific discovery and public
communication.Comment: Published in IEEE VIS 2019, 9 pages of content with 2 pages of
references, 12 figure
Perspective-aware texture analysis and synthesis
The original publication is available at www.springerlink.comInternational audienceThis paper presents a novel texture synthesis scheme for anisotropic 2D textures based on perspective feature analysis and energy optimization. Given an example texture, the synthesis process starts with analyzing the texel (TEXture ELement) scale variations to obtain the perspective map (scale map). Feature mask and simple user-assisted scale extraction operations including slant and tilt angles assignment and scale value editing are applied. The scale map represents the global variations of the texel scales in the sample texture. Then, we extend 2D texture optimization techniques to synthesize these kinds of perspectively featured textures. The non-parametric texture optimization approach is integrated with histogram matching, which forces the global statics of the texel scale variations of the synthesized texture to match those of the example. We also demonstrate that our method is well-suited for image completion of a perspectively featured texture region in a digital photo
Free-viewpoint Indoor Neural Relighting from Multi-view Stereo
We introduce a neural relighting algorithm for captured indoors scenes, that
allows interactive free-viewpoint navigation. Our method allows illumination to
be changed synthetically, while coherently rendering cast shadows and complex
glossy materials. We start with multiple images of the scene and a 3D mesh
obtained by multi-view stereo (MVS) reconstruction. We assume that lighting is
well-explained as the sum of a view-independent diffuse component and a
view-dependent glossy term concentrated around the mirror reflection direction.
We design a convolutional network around input feature maps that facilitate
learning of an implicit representation of scene materials and illumination,
enabling both relighting and free-viewpoint navigation. We generate these input
maps by exploiting the best elements of both image-based and physically-based
rendering. We sample the input views to estimate diffuse scene irradiance, and
compute the new illumination caused by user-specified light sources using path
tracing. To facilitate the network's understanding of materials and synthesize
plausible glossy reflections, we reproject the views and compute mirror images.
We train the network on a synthetic dataset where each scene is also
reconstructed with MVS. We show results of our algorithm relighting real indoor
scenes and performing free-viewpoint navigation with complex and realistic
glossy reflections, which so far remained out of reach for view-synthesis
techniques
Approximation of tensor fields on surfaces of arbitrary topology based on local Monge parametrizations
We introduce a new method, the Local Monge Parametrizations (LMP) method, to
approximate tensor fields on general surfaces given by a collection of local
parametrizations, e.g.~as in finite element or NURBS surface representations.
Our goal is to use this method to solve numerically tensor-valued partial
differential equations (PDE) on surfaces. Previous methods use scalar
potentials to numerically describe vector fields on surfaces, at the expense of
requiring higher-order derivatives of the approximated fields and limited to
simply connected surfaces, or represent tangential tensor fields as tensor
fields in 3D subjected to constraints, thus increasing the essential number of
degrees of freedom. In contrast, the LMP method uses an optimal number of
degrees of freedom to represent a tensor, is general with regards to the
topology of the surface, and does not increase the order of the PDEs governing
the tensor fields. The main idea is to construct maps between the element
parametrizations and a local Monge parametrization around each node. We test
the LMP method by approximating in a least-squares sense different vector and
tensor fields on simply connected and genus-1 surfaces. Furthermore, we apply
the LMP method to two physical models on surfaces, involving a tension-driven
flow (vector-valued PDE) and nematic ordering (tensor-valued PDE). The LMP
method thus solves the long-standing problem of the interpolation of tensors on
general surfaces with an optimal number of degrees of freedom.Comment: 16 pages, 6 figure
RGBD Datasets: Past, Present and Future
Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style
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