18,619 research outputs found
An Image Morphing Technique Based on Optimal Mass Preserving Mapping
©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.896637Image morphing, or image interpolation in the time domain, deals with the metamorphosis of one image into another. In this paper, a new class of image morphing algorithms is proposed based on the theory of optimal mass transport. The 2 mass moving energy functional is modified by adding an intensity penalizing term, in order to reduce the undesired double exposure effect. It is an intensity-based approach and, thus, is parameter free. The optimal warping function is computed using an iterative gradient descent approach. This proposed morphing method is also extended to doubly connected domains using a harmonic parameterization technique, along with finite-element methods
Fast and robust 3D feature extraction from sparse point clouds
Matching 3D point clouds, a critical operation in map building and localization, is difficult with Velodyne-type sensors due to the sparse and non-uniform point clouds that they produce. Standard methods from dense 3D point clouds are generally not effective. In this paper, we describe a featurebased approach using Principal Components Analysis (PCA) of neighborhoods of points, which results in mathematically principled line and plane features. The key contribution in this work is to show how this type of feature extraction can be done efficiently and robustly even on non-uniformly sampled point clouds. The resulting detector runs in real-time and can be easily tuned to have a low false positive rate, simplifying data association. We evaluate the performance of our algorithm on an autonomous car at the MCity Test Facility using a Velodyne HDL-32E, and we compare our results against the state-of-theart NARF keypoint detector. © 2016 IEEE
Learning shape correspondence with anisotropic convolutional neural networks
Establishing correspondence between shapes is a fundamental problem in
geometry processing, arising in a wide variety of applications. The problem is
especially difficult in the setting of non-isometric deformations, as well as
in the presence of topological noise and missing parts, mainly due to the
limited capability to model such deformations axiomatically. Several recent
works showed that invariance to complex shape transformations can be learned
from examples. In this paper, we introduce an intrinsic convolutional neural
network architecture based on anisotropic diffusion kernels, which we term
Anisotropic Convolutional Neural Network (ACNN). In our construction, we
generalize convolutions to non-Euclidean domains by constructing a set of
oriented anisotropic diffusion kernels, creating in this way a local intrinsic
polar representation of the data (`patch'), which is then correlated with a
filter. Several cascades of such filters, linear, and non-linear operators are
stacked to form a deep neural network whose parameters are learned by
minimizing a task-specific cost. We use ACNNs to effectively learn intrinsic
dense correspondences between deformable shapes in very challenging settings,
achieving state-of-the-art results on some of the most difficult recent
correspondence benchmarks
Mars Express measurements of surface albedo changes over 2004 - 2010
The pervasive Mars dust is continually transported between the surface and
the atmosphere. When on the surface, dust increases the albedo of darker
underlying rocks and regolith, which modifies climate energy balance and must
be quantified. Remote observation of surface albedo absolute value and albedo
change is however complicated by dust itself when lifted in the atmosphere.
Here we present a method to calculate and map the bolometric solar
hemispherical albedo of the Martian surface using the 2004 - 2010 OMEGA imaging
spectrometer dataset. This method takes into account aerosols radiative
transfer, surface photometry, and instrumental issues such as registration
differences between visible and near-IR detectors. Resulting albedos are on
average 17% higher than previous estimates for bright surfaces while similar
for dark surfaces. We observed that surface albedo changes occur mostly during
the storm season due to isolated events. The main variations are observed
during the 2007 global dust storm and during the following year. A wide variety
of change timings are detected such as dust deposited and then cleaned over a
Martian year, areas modified only during successive global dust storms, and
perennial changes over decades. Both similarities and differences with previous
global dust storms are observed. While an optically thin layer of bright dust
is involved in most changes, this coating turns out to be sufficient to mask
underlying mineralogical near-IR spectral signatures. Overall, changes result
from apparently erratic events; however, a cyclic evolution emerges for some
(but not all) areas over long timescales
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