60 research outputs found
SO(3)-invariant asymptotic observers for dense depth field estimation based on visual data and known camera motion
In this paper, we use known camera motion associated to a video sequence of a
static scene in order to estimate and incrementally refine the surrounding
depth field. We exploit the SO(3)-invariance of brightness and depth fields
dynamics to customize standard image processing techniques. Inspired by the
Horn-Schunck method, we propose a SO(3)-invariant cost to estimate the depth
field. At each time step, this provides a diffusion equation on the unit
Riemannian sphere that is numerically solved to obtain a real time depth field
estimation of the entire field of view. Two asymptotic observers are derived
from the governing equations of dynamics, respectively based on optical flow
and depth estimations: implemented on noisy sequences of synthetic images as
well as on real data, they perform a more robust and accurate depth estimation.
This approach is complementary to most methods employing state observers for
range estimation, which uniquely concern single or isolated feature points.Comment: Submitte
Efficient Evaluation of the Number of False Alarm Criterion
This paper proposes a method for computing efficiently the significance of a
parametric pattern inside a binary image. On the one hand, a-contrario
strategies avoid the user involvement for tuning detection thresholds, and
allow one to account fairly for different pattern sizes. On the other hand,
a-contrario criteria become intractable when the pattern complexity in terms of
parametrization increases. In this work, we introduce a strategy which relies
on the use of a cumulative space of reduced dimensionality, derived from the
coupling of a classic (Hough) cumulative space with an integral histogram
trick. This space allows us to store partial computations which are required by
the a-contrario criterion, and to evaluate the significance with a lower
computational cost than by following a straightforward approach. The method is
illustrated on synthetic examples on patterns with various parametrizations up
to five dimensions. In order to demonstrate how to apply this generic concept
in a real scenario, we consider a difficult crack detection task in still
images, which has been addressed in the literature with various local and
global detection strategies. We model cracks as bounded segments, detected by
the proposed a-contrario criterion, which allow us to introduce additional
spatial constraints based on their relative alignment. On this application, the
proposed strategy yields state-of the-art results, and underlines its potential
for handling complex pattern detection tasks
Estimation dense de profondeur combinant approches variationnelles et observateurs asymptotiques
Session "Articles"National audienceCet article propose une nouvelle approche pour estimer en temps réel la carte de profondeur instantanée à partir de données inertielles et images fournies par une caméra en mouvement libre dans une scène statique. Une fonction coût invariante par rotation est introduite. Sa minimisation conduit à une estimation de la carte de profondeur, solution d'une équation de diffusion sur la sphère Riemannienne de l'espace à trois dimensions. Transcrite en coordonnées pinhole, cette équation est résolue numériquement et donne une première estimation de la carte de profondeur sur la totalité du champ couvert par la caméra. Un observateur asymptotique reposant sur un modèle géométrique de l'évolution du champ de profondeur à partir des données inertielles, permet ensuite d'affiner continûment cette estimation. Cette approche diffère notablement de la plupart des méthodes actuelles qui estiment la carte de profondeur à partir de plusieurs vues stéréo combinées avec des expansions de régions, ou encore des stratégies probabilistes d'affinement incrémental. Des analyses quantitatives des estimations obtenues sur des données de synthèse illustrent l'intérêt de la méthode proposée. De premiers résultats sur données réelles confirment les simulations sur données synthétique
Constraining Relative Camera Pose Estimation with Pedestrian Detector-Based Correspondence Filters
International audienceA prerequisite for using smart camera networks effectively is a precise extrinsic calibration of the camera sensors , either in a fixed coordinate system, or relatively to each other. For cameras with partly overlapping fields of view, the relative pose estimation may be directly performed on or assisted by the video content obtained during scene analysis. In typical conditions however (wide baseline, repetitive patterns, homogeneous appearance of pedestrians), the pose estimation is imprecise and very often is affected by large errors in weakly constrained areas of the field of view. In this work, we propose to rely on progressively stricter constraints on the feature association between the camera views, guided by a pedestrian detector and a re-identification algorithm respectively. The results show that the two strategies are effective in alleviating the ambiguity which is due to the similar appearance of pedestrians in such scenes, and in improving the relative pose estimation
Evaluating Crowd Density Estimators via Their Uncertainty Bounds
In this work, we use the Belief Function Theory which extends the
probabilistic framework in order to provide uncertainty bounds to different
categories of crowd density estimators. Our method allows us to compare the
multi-scale performance of the estimators, and also to characterize their
reliability for crowd monitoring applications requiring varying degrees of
prudence
Determining Epipole Location Integrity by Multimodal Sampling
International audienceIn urban cluttered scenes, a photo provided by a wear-able camera may be used by a walking law-enforcement agent as an additional source of information for localizing themselves, or elements of interest related to public safety and security. In this work, we study the problem of locating the epipole, corresponding to the position of the moving camera, in the field of view of a reference camera. We show that the presence of outliers in the standard pipeline for camera relative pose estimation not only prevents the correct estimation of the epipole localization but also degrades the standard uncertainty propagation for the epipole position. We propose a robust method for constructing an epipole location map, and we evaluate its accuracy as well as its level of integrity with respect to standard approaches
Augmenting Deep Learning Performance in an Evidential Multiple Classifier System
International audienceThe main objective of this work is to study the applicability of ensemble methods in the context of deep learning with limited amounts of labeled data. We exploit an ensemble of neural networks derived using Monte Carlo dropout, along with an ensemble of SVM classifiers which owes its effectiveness to the hand-crafted features used as inputs and to an active learning procedure. In order to leverage each classifier's respective strengths, we combine them in an evidential framework, which models specifically their imprecision and uncertainty. The application we consider in order to illustrate the interest of our Multiple Classifier System is pedestrian detection in high-density crowds, which is ideally suited for its difficulty, cost of labeling and intrinsic imprecision of annotation data. We show that the fusion resulting from the effective modeling of uncertainty allows for performance improvement, and at the same time, for a deeper interpretation of the result in terms of commitment of the decision
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
Uncertainty quantification is critical for deploying deep neural networks
(DNNs) in real-world applications. An Auxiliary Uncertainty Estimator (AuxUE)
is one of the most effective means to estimate the uncertainty of the main task
prediction without modifying the main task model. To be considered robust, an
AuxUE must be capable of maintaining its performance and triggering higher
uncertainties while encountering Out-of-Distribution (OOD) inputs, i.e., to
provide robust aleatoric and epistemic uncertainty. However, for vision
regression tasks, current AuxUE designs are mainly adopted for aleatoric
uncertainty estimates, and AuxUE robustness has not been explored. In this
work, we propose a generalized AuxUE scheme for more robust uncertainty
quantification on regression tasks. Concretely, to achieve a more robust
aleatoric uncertainty estimation, different distribution assumptions are
considered for heteroscedastic noise, and Laplace distribution is finally
chosen to approximate the prediction error. For epistemic uncertainty, we
propose a novel solution named Discretization-Induced Dirichlet pOsterior
(DIDO), which models the Dirichlet posterior on the discretized prediction
error. Extensive experiments on age estimation, monocular depth estimation, and
super-resolution tasks show that our proposed method can provide robust
uncertainty estimates in the face of noisy inputs and that it can be scalable
to both image-level and pixel-wise tasks.Comment: 22 page
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