2,773 research outputs found
Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion
Large field-of-view fisheye lens cameras have attracted more and more
researchers' attention in the field of robotics. However, there does not exist
a convenient off-the-shelf stereo rectification approach which can be applied
directly to fisheye stereo rig. One obvious drawback of existing methods is
that the resampling distortion (which is defined as the loss of pixels due to
under-sampling and the creation of new pixels due to over-sampling during
rectification process) is severe if we want to obtain a rectification with
epipolar line (not epipolar circle) constraint. To overcome this weakness, we
propose a novel pixel-wise local homography technique for stereo rectification.
First, we prove that there indeed exist enough degrees of freedom to apply
pixel-wise local homography for stereo rectification. Then we present a method
to exploit these freedoms and the solution via an optimization framework.
Finally, the robustness and effectiveness of the proposed method have been
verified on real fisheye lens images. The rectification results show that the
proposed approach can effectively reduce the resampling distortion in
comparison with existing methods while satisfying the epipolar line constraint.
By employing the proposed method, dense stereo matching and 3D reconstruction
for fisheye lens camera become as easy as perspective lens cameras
Synthetic aperture radar/LANDSAT MSS image registration
Algorithms and procedures necessary to merge aircraft synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) imagery were determined. The design of a SAR/LANDSAT data merging system was developed. Aircraft SAR images were registered to the corresponding LANDSAT MSS scenes and were the subject of experimental investigations. Results indicate that the registration of SAR imagery with LANDSAT MSS imagery is feasible from a technical viewpoint, and useful from an information-content viewpoint
Concepts for on-board satellite image registration, volume 1
The NASA-NEEDS program goals present a requirement for on-board signal processing to achieve user-compatible, information-adaptive data acquisition. One very specific area of interest is the preprocessing required to register imaging sensor data which have been distorted by anomalies in subsatellite-point position and/or attitude control. The concepts and considerations involved in using state-of-the-art positioning systems such as the Global Positioning System (GPS) in concert with state-of-the-art attitude stabilization and/or determination systems to provide the required registration accuracy are discussed with emphasis on assessing the accuracy to which a given image picture element can be located and identified, determining those algorithms required to augment the registration procedure and evaluating the technology impact on performing these procedures on-board the satellite
A Unified Framework for Multi-Sensor HDR Video Reconstruction
One of the most successful approaches to modern high quality HDR-video
capture is to use camera setups with multiple sensors imaging the scene through
a common optical system. However, such systems pose several challenges for HDR
reconstruction algorithms. Previous reconstruction techniques have considered
debayering, denoising, resampling (align- ment) and exposure fusion as separate
problems. In contrast, in this paper we present a unifying approach, performing
HDR assembly directly from raw sensor data. Our framework includes a camera
noise model adapted to HDR video and an algorithm for spatially adaptive HDR
reconstruction based on fitting of local polynomial approximations to observed
sensor data. The method is easy to implement and allows reconstruction to an
arbitrary resolution and output mapping. We present an implementation in CUDA
and show real-time performance for an experimental 4 Mpixel multi-sensor HDR
video system. We further show that our algorithm has clear advantages over
existing methods, both in terms of flexibility and reconstruction quality
Bayesian Trend Filtering
We develop a fully Bayesian hierarchical model for trend filtering, itself a
new development in nonparametric, univariate regression. The framework more
broadly applies to the generalized lasso, but focus is on Bayesian trend
filtering. We compare two shrinkage priors, double exponential and generalized
double Pareto. A simulation study, comparing Bayesian trend filtering to the
original formulation and a number of other popular methods shows our method to
improve estimation error while maintaining if not improving coverage
probability. Two time series data sets demonstrate Bayesian trend filtering's
robustness to possible violations of its assumptions
The adaptive patched particle filter and its implementation
There are numerous contexts where one wishes to describe the state of a
randomly evolving system. Effective solutions combine models that quantify the
underlying uncertainty with available observational data to form relatively
optimal estimates for the uncertainty in the system state. Stochastic
differential equations are often used to mathematically model the underlying
system. The Kusuoka-Lyons-Victoir (KLV) approach is a higher order particle
method for approximating the weak solution of a stochastic differential
equation that uses a weighted set of scenarios to approximate the evolving
probability distribution to a high order of accuracy. The algorithm can be
performed by integrating along a number of carefully selected bounded variation
paths and the iterated application of the KLV method has a tendency for the
number of particles to increase. Together with local dynamic recombination that
simplifies the support of discrete measure without harming the accuracy of the
approximation, the KLV method becomes eligible to solve the filtering problem
for which one has to maintain an accurate description of the ever-evolving
conditioned measure. Besides the alternate application of the KLV method and
recombination for the entire family of particles, we make use of the smooth
nature of likelihood to lead some of the particles immediately to the next
observation time and to build an algorithm that is a form of automatic high
order adaptive importance sampling
Target Tracking via Crowdsourcing: A Mechanism Design Approach
In this paper, we propose a crowdsourcing based framework for myopic target
tracking by designing an incentive-compatible mechanism based optimal auction
in a wireless sensor network (WSN) containing sensors that are selfish and
profit-motivated. For typical WSNs which have limited bandwidth, the fusion
center (FC) has to distribute the total number of bits that can be transmitted
from the sensors to the FC among the sensors. To accomplish the task, the FC
conducts an auction by soliciting bids from the selfish sensors, which reflect
how much they value their energy cost. Furthermore, the rationality and
truthfulness of the sensors are guaranteed in our model. The final problem is
formulated as a multiple-choice knapsack problem (MCKP), which is solved by the
dynamic programming method in pseudo-polynomial time. Simulation results show
the effectiveness of our proposed approach in terms of both the tracking
performance and lifetime of the sensor network.Comment: 13 pages, 11 figures, IEEE Signal Processing Transactio
Complementarity of PALM and SOFI for super-resolution live cell imaging of focal adhesions
Live cell imaging of focal adhesions requires a sufficiently high temporal
resolution, which remains a challenging task for super-resolution microscopy.
We have addressed this important issue by combining photo-activated
localization microscopy (PALM) with super-resolution optical fluctuation
imaging (SOFI). Using simulations and fixed cell focal adhesion images, we
investigated the complementarity between PALM and SOFI in terms of spatial and
temporal resolution. This PALM-SOFI framework was used to image focal adhesions
in living cells, while obtaining a temporal resolution below 10 s. We
visualized the dynamics of focal adhesions, and revealed local mean velocities
around 190 nm per minute. The complementarity of PALM and SOFI was assessed in
detail with a methodology that integrates a quantitative resolution and
signal-to-noise metric. This PALM and SOFI concept provides an enlarged
quantitative imaging framework, allowing unprecedented functional exploration
of focal adhesions through the estimation of molecular parameters such as the
fluorophore density and the photo-activation and photo-switching rates
Continious-time Importance Sampling: Monte Carlo Methods which Avoid Time-discretisation Error
In this paper we develop a continuous-time sequential importance sampling
(CIS) algorithm which eliminates time-discretisation errors and provides online
unbiased estimation for continuous time Markov processes, in particular for
diffusions. Our work removes the strong conditions imposed by the EA and thus
extends significantly the class of discretisation error-free MC methods for
diffusions. The reason that CIS can be applied more generally than EA is that
it no longer works on the path space of the SDE. Instead it uses proposal
distributions for the transition density of the diffusion, and proposal
distributions that are absolutely continuous with respect to the true
transition density exist for general SDEs
A Framework for SAR-Optical Stereogrammetry over Urban Areas
Currently, numerous remote sensing satellites provide a huge volume of
diverse earth observation data. As these data show different features regarding
resolution, accuracy, coverage, and spectral imaging ability, fusion techniques
are required to integrate the different properties of each sensor and produce
useful information. For example, synthetic aperture radar (SAR) data can be
fused with optical imagery to produce 3D information using stereogrammetric
methods. The main focus of this study is to investigate the possibility of
applying a stereogrammetry pipeline to very-high-resolution (VHR) SAR-optical
image pairs. For this purpose, the applicability of semi-global matching is
investigated in this unconventional multi-sensor setting. To support the image
matching by reducing the search space and accelerating the identification of
correct, reliable matches, the possibility of establishing an epipolarity
constraint for VHR SAR-optical image pairs is investigated as well. In
addition, it is shown that the absolute geolocation accuracy of VHR optical
imagery with respect to VHR SAR imagery such as provided by TerraSAR-X can be
improved by a multi-sensor block adjustment formulation based on rational
polynomial coefficients. Finally, the feasibility of generating point clouds
with a median accuracy of about 2m is demonstrated and confirms the potential
of 3D reconstruction from SAR-optical image pairs over urban areas.Comment: This is the pre-acceptance version, to read the final version, please
go to ISPRS Journal of Photogrammetry and Remote Sensing on ScienceDirec
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