324,462 research outputs found
Robust similarity registration technique for volumetric shapes represented by characteristic functions
This paper proposes a novel similarity registration technique for volumetric shapes implicitly represented by their characteristic functions (CFs). Here, the calculation of rotation parameters is considered as a spherical crosscorrelation problem and the solution is therefore found using the standard phase correlation technique facilitated by principal components analysis (PCA).Thus, fast Fourier transform (FFT) is employed to vastly improve efficiency and robustness. Geometric moments are then used for shape scale estimation which is independent from rotation and translation parameters. It is numericallydemonstrated that our registration method is able to handle shapes with various topologies and robust to noise and initial poses. Further validation of our method is performed by registering a lung database
Estimation rapide des paramètres d'un signal à phase polynomiale
National audiencePolynomial phase signals belong to a wide class of non-stationary signals used for modeling and engineering applications. In this paper, we take benefits of some advances in robust estimation in order to propose a new algorithm for estimating the parameters of a polynomial phase signal. The advantages of this algorithm are being fast and being robust to the shape of the noise
Parameter Estimation for Polynomial Phase Signals With a Fast and Robust Algorithm
International audiencePolynomial phase signals belong to a wide class of nonstationary signals used for modeling and engineering applications. In this paper, we take benefits of some advances in robust estimation in order to propose a new algorithm for estimating the parameters of a polynomial phase signal. This algorithm has the advantages to be fast and its structure is robust to the shape of the noise
Optimal Pose and Shape Estimation for Category-level 3D Object Perception
We consider a category-level perception problem, where one is given 3D sensor
data picturing an object of a given category (e.g. a car), and has to
reconstruct the pose and shape of the object despite intra-class variability
(i.e. different car models have different shapes). We consider an active shape
model, where -- for an object category -- we are given a library of potential
CAD models describing objects in that category, and we adopt a standard
formulation where pose and shape estimation are formulated as a non-convex
optimization. Our first contribution is to provide the first certifiably
optimal solver for pose and shape estimation. In particular, we show that
rotation estimation can be decoupled from the estimation of the object
translation and shape, and we demonstrate that (i) the optimal object rotation
can be computed via a tight (small-size) semidefinite relaxation, and (ii) the
translation and shape parameters can be computed in closed-form given the
rotation. Our second contribution is to add an outlier rejection layer to our
solver, hence making it robust to a large number of misdetections. Towards this
goal, we wrap our optimal solver in a robust estimation scheme based on
graduated non-convexity. To further enhance robustness to outliers, we also
develop the first graph-theoretic formulation to prune outliers in
category-level perception, which removes outliers via convex hull and maximum
clique computations; the resulting approach is robust to 70%-90% outliers. Our
third contribution is an extensive experimental evaluation. Besides providing
an ablation study on a simulated dataset and on the PASCAL3D+ dataset, we
combine our solver with a deep-learned keypoint detector, and show that the
resulting approach improves over the state of the art in vehicle pose
estimation in the ApolloScape datasets
Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach
In standard clinical within-subject analyses of event-related fMRI data, two
steps are usually performed separately: detection of brain activity and
estimation of the hemodynamic response. Because these two steps are inherently
linked, we adopt the so-called region-based Joint Detection-Estimation (JDE)
framework that addresses this joint issue using a multivariate inference for
detection and estimation. JDE is built by making use of a regional bilinear
generative model of the BOLD response and constraining the parameter estimation
by physiological priors using temporal and spatial information in a Markovian
modeling. In contrast to previous works that use Markov Chain Monte Carlo
(MCMC) techniques to approximate the resulting intractable posterior
distribution, we recast the JDE into a missing data framework and derive a
Variational Expectation-Maximization (VEM) algorithm for its inference. A
variational approximation is used to approximate the Markovian model in the
unsupervised spatially adaptive JDE inference, which allows fine automatic
tuning of spatial regularisation parameters. It follows a new algorithm that
exhibits interesting properties compared to the previously used MCMC-based
approach. Experiments on artificial and real data show that VEM-JDE is robust
to model mis-specification and provides computational gain while maintaining
good performance in terms of activation detection and hemodynamic shape
recovery
Curved Gabor Filters for Fingerprint Image Enhancement
Gabor filters play an important role in many application areas for the
enhancement of various types of images and the extraction of Gabor features.
For the purpose of enhancing curved structures in noisy images, we introduce
curved Gabor filters which locally adapt their shape to the direction of flow.
These curved Gabor filters enable the choice of filter parameters which
increase the smoothing power without creating artifacts in the enhanced image.
In this paper, curved Gabor filters are applied to the curved ridge and valley
structure of low-quality fingerprint images. First, we combine two orientation
field estimation methods in order to obtain a more robust estimation for very
noisy images. Next, curved regions are constructed by following the respective
local orientation and they are used for estimating the local ridge frequency.
Lastly, curved Gabor filters are defined based on curved regions and they are
applied for the enhancement of low-quality fingerprint images. Experimental
results on the FVC2004 databases show improvements of this approach in
comparison to state-of-the-art enhancement methods
Shape constrained additive models
A framework is presented for generalized additive modelling under shape constraints on the component functions of the linear predictor of the GAM. We represent shape constrained model components by mildly non-linear extensions of P-splines. Models can contain multiple shape constrained and unconstrained terms as well as shape constrained multi-dimensional smooths. The constraints considered are on the sign of the first or/and the second derivatives of the smooth terms. A key advantage of the approach is that it facilitates efficient estimation of smoothing parameters as an integral part of model estimation, via GCV or AIC, and numerically robust algorithms for this are presented. We also derive simulation free approximate Bayesian confidence intervals for the smooth components, which are shown to achieve close to nominal coverage probabilities. Applications are presented using real data examples including the risk of disease in relation to proximity to municipal incinerators and the association between air pollution and health
ViSE: Vision-Based 3D Online Shape Estimation of Continuously Deformable Robots
The precise control of soft and continuum robots requires knowledge of their
shape. The shape of these robots has, in contrast to classical rigid robots,
infinite degrees of freedom. To partially reconstruct the shape, proprioceptive
techniques use built-in sensors resulting in inaccurate results and increased
fabrication complexity. Exteroceptive methods so far rely on placing reflective
markers on all tracked components and triangulating their position using
multiple motion-tracking cameras. Tracking systems are expensive and infeasible
for deformable robots interacting with the environment due to marker occlusion
and damage. Here, we present a regression approach for 3D shape estimation
using a convolutional neural network. The proposed approach takes advantage of
data-driven supervised learning and is capable of real-time marker-less shape
estimation during inference. Two images of a robotic system are taken
simultaneously at 25 Hz from two different perspectives, and are fed to the
network, which returns for each pair the parameterized shape. The proposed
approach outperforms marker-less state-of-the-art methods by a maximum of 4.4%
in estimation accuracy while at the same time being more robust and requiring
no prior knowledge of the shape. The approach can be easily implemented due to
only requiring two color cameras without depth and not needing an explicit
calibration of the extrinsic parameters. Evaluations on two types of soft
robotic arms and a soft robotic fish demonstrate our method's accuracy and
versatility on highly deformable systems in real-time. The robust performance
of the approach against different scene modifications (camera alignment and
brightness) suggests its generalizability to a wider range of experimental
setups, which will benefit downstream tasks such as robotic grasping and
manipulation
A convexity approach to dynamic output feedback robust MPC for LPV systems with bounded disturbances
International audienceA convexity approach to dynamic output feedback robust model predictive control (OFRMPC) is proposed for linear parameter varying (LPV) systems with bounded disturbances. At each sampling time, the model parameters and disturbances are assumed to be unknown but bounded within pre-specified convex sets. Robust stability conditions on the augmented closed-loop system are derived using the techniques of robust positively invariant (RPI) set and the S-procedure. A convexity method reformulates the non-convex bilinear matrix inequalities (BMIs) problem as a convex optimization one such that the on-line computational burden is significantly reduced. The on-line optimized dynamic output feedback controller parameters steer the augmented states to converge within RPI sets and recursive feasibility of the optimization problem is guaranteed. Furthermore, bounds of the estimation error set are refreshed by updating the shape matrix of the future ellipsoidal estimation error set. The dynamic OFRMPC approach guarantees that the disturbance-free augmented closed-loop system (without consideration of disturbances) converges to the origin. In addition, when the system is subject to bounded disturbances, the augmented closed-loop system converges to a neighborhood of the origin. Two simulation examples are given to verify the effectiveness of the approach
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