12,995 research outputs found
SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation
State-of-the-art scene flow algorithms pursue the conflicting targets of
accuracy, run time, and robustness. With the successful concept of pixel-wise
matching and sparse-to-dense interpolation, we push the limits of scene flow
estimation. Avoiding strong assumptions on the domain or the problem yields a
more robust algorithm. This algorithm is fast because we avoid explicit
regularization during matching, which allows an efficient computation. Using
image information from multiple time steps and explicit visibility prediction
based on previous results, we achieve competitive performances on different
data sets. Our contributions and results are evaluated in comparative
experiments. Overall, we present an accurate scene flow algorithm that is
faster and more generic than any individual benchmark leader.Comment: arXiv admin note: text overlap with arXiv:1710.1009
Temporal Unknown Incremental Clustering (TUIC) Model for Analysis of Traffic Surveillance Videos
Optimized scene representation is an important characteristic of a framework
for detecting abnormalities on live videos. One of the challenges for detecting
abnormalities in live videos is real-time detection of objects in a
non-parametric way. Another challenge is to efficiently represent the state of
objects temporally across frames. In this paper, a Gibbs sampling based
heuristic model referred to as Temporal Unknown Incremental Clustering (TUIC)
has been proposed to cluster pixels with motion. Pixel motion is first detected
using optical flow and a Bayesian algorithm has been applied to associate
pixels belonging to similar cluster in subsequent frames. The algorithm is fast
and produces accurate results in time, where is the number of
clusters and the number of pixels. Our experimental validation with
publicly available datasets reveals that the proposed framework has good
potential to open-up new opportunities for real-time traffic analysis
B-spline Shape from Motion & Shading: An Automatic Free-form Surface Modeling for Face Reconstruction
Recently, many methods have been proposed for face reconstruction from
multiple images, most of which involve fundamental principles of Shape from
Shading and Structure from motion. However, a majority of the methods just
generate discrete surface model of face. In this paper, B-spline Shape from
Motion and Shading (BsSfMS) is proposed to reconstruct continuous B-spline
surface for multi-view face images, according to an assumption that shading and
motion information in the images contain 1st- and 0th-order derivative of
B-spline face respectively. Face surface is expressed as a B-spline surface
that can be reconstructed by optimizing B-spline control points. Therefore,
normals and 3D feature points computed from shading and motion of images
respectively are used as the 1st- and 0th- order derivative information, to be
jointly applied in optimizing the B-spline face. Additionally, an IMLS
(iterative multi-least-square) algorithm is proposed to handle the difficult
control point optimization. Furthermore, synthetic samples and LFW dataset are
introduced and conducted to verify the proposed approach, and the experimental
results demonstrate the effectiveness with different poses, illuminations,
expressions etc., even with wild images.Comment: 9 pages, 6 figure
Online Algorithms for Factorization-Based Structure from Motion
We present a family of online algorithms for real-time factorization-based
structure from motion, leveraging a relationship between incremental singular
value decomposition and recently proposed methods for online matrix completion.
Our methods are orders of magnitude faster than previous state of the art, can
handle missing data and a variable number of feature points, and are robust to
noise and sparse outliers. We demonstrate our methods on both real and
synthetic sequences and show that they perform well in both online and batch
settings. We also provide an implementation which is able to produce 3D models
in real time using a laptop with a webcam
Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance
Probabilistic sampling-based algorithms, such as the probabilistic roadmap
(PRM) and the rapidly-exploring random tree (RRT) algorithms, represent one of
the most successful approaches to robotic motion planning, due to their strong
theoretical properties (in terms of probabilistic completeness or even
asymptotic optimality) and remarkable practical performance. Such algorithms
are probabilistic in that they compute a path by connecting independently and
identically distributed random points in the configuration space. Their
randomization aspect, however, makes several tasks challenging, including
certification for safety-critical applications and use of offline computation
to improve real-time execution. Hence, an important open question is whether
similar (or better) theoretical guarantees and practical performance could be
obtained by considering deterministic, as opposed to random sampling sequences.
The objective of this paper is to provide a rigorous answer to this question.
Specifically, we first show that PRM, for a certain selection of tuning
parameters and deterministic low-dispersion sampling sequences, is
deterministically asymptotically optimal. Second, we characterize the
convergence rate, and we find that the factor of sub-optimality can be very
explicitly upper-bounded in terms of the l2-dispersion of the sampling sequence
and the connection radius of PRM. Third, we show that an asymptotically optimal
version of PRM exists with computational and space complexity arbitrarily close
to O(n) (the theoretical lower bound), where n is the number of points in the
sequence. This is in stark contrast to the O(n logn) complexity results for
existing asymptotically-optimal probabilistic planners. Finally, through
numerical experiments, we show that planning with deterministic low-dispersion
sampling generally provides superior performance in terms of path cost and
success rate
EndoSensorFusion: Particle Filtering-Based Multi-sensory Data Fusion with Switching State-Space Model for Endoscopic Capsule Robots
A reliable, real time multi-sensor fusion functionality is crucial for
localization of actively controlled capsule endoscopy robots, which are an
emerging, minimally invasive diagnostic and therapeutic technology for the
gastrointestinal (GI) tract. In this study, we propose a novel multi-sensor
fusion approach based on a particle filter that incorporates an online
estimation of sensor reliability and a non-linear kinematic model learned by a
recurrent neural network. Our method sequentially estimates the true robot pose
from noisy pose observations delivered by multiple sensors. We experimentally
test the method using 5 degree-of-freedom (5-DoF) absolute pose measurement by
a magnetic localization system and a 6-DoF relative pose measurement by visual
odometry. In addition, the proposed method is capable of detecting and handling
sensor failures by ignoring corrupted data, providing the robustness expected
of a medical device. Detailed analyses and evaluations are presented using
ex-vivo experiments on a porcine stomach model prove that our system achieves
high translational and rotational accuracies for different types of endoscopic
capsule robot trajectories.Comment: submitted to ICRA 2018. arXiv admin note: text overlap with
arXiv:1705.0619
Robotics Meets Cosmetic Dermatology: Development of a Novel Vision-Guided System for Skin Photo-Rejuvenation
In this paper, we present a novel robotic system for skin photo-rejuvenation
procedures, which can uniformly deliver the laser's energy over the skin of the
face. The robotised procedure is performed by a manipulator whose end-effector
is instrumented with a depth sensor, a thermal camera, and a cosmetic laser
generator. To plan the heat stimulating trajectories for the laser, the system
computes the surface model of the face and segments it into seven regions that
are automatically filled with laser shots. We report experimental results with
human subjects to validate the performance of the system. To the best of the
author's knowledge, this is the first time that facial skin rejuvenation has
been automated by robot manipulators.Comment: 11 pages, 16 figure
Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data
The Shape Interaction Matrix (SIM) is one of the earliest approaches to
performing subspace clustering (i.e., separating points drawn from a union of
subspaces). In this paper, we revisit the SIM and reveal its connections to
several recent subspace clustering methods. Our analysis lets us derive a
simple, yet effective algorithm to robustify the SIM and make it applicable to
realistic scenarios where the data is corrupted by noise. We justify our method
by intuitive examples and the matrix perturbation theory. We then show how this
approach can be extended to handle missing data, thus yielding an efficient and
general subspace clustering algorithm. We demonstrate the benefits of our
approach over state-of-the-art subspace clustering methods on several
challenging motion segmentation and face clustering problems, where the data
includes corrupted and missing measurements.Comment: This is an extended version of our iccv15 pape
Finite element appendange equations for hybrid coordinate dynamic analysis
Development of hybrid coordinate equations of motion for finite element model of flexible appendage attached to rigid base undergoing unrestricted motion
Topology-Aware Non-Rigid Point Cloud Registration
In this paper, we introduce a non-rigid registration pipeline for pairs of
unorganized point clouds that may be topologically different. Standard warp
field estimation algorithms, even under robust, discontinuity-preserving
regularization, tend to produce erratic motion estimates on boundaries
associated with `close-to-open' topology changes. We overcome this limitation
by exploiting backward motion: in the opposite motion direction, a
`close-to-open' event becomes `open-to-close', which is by default handled
correctly. At the core of our approach lies a general, topology-agnostic warp
field estimation algorithm, similar to those employed in recently introduced
dynamic reconstruction systems from RGB-D input. We improve motion estimation
on boundaries associated with topology changes in an efficient post-processing
phase. Based on both forward and (inverted) backward warp hypotheses, we
explicitly detect regions of the deformed geometry that undergo topological
changes by means of local deformation criteria and broadly classify them as
`contacts' or `separations'. Subsequently, the two motion hypotheses are
seamlessly blended on a local basis, according to the type and proximity of
detected events. Our method achieves state-of-the-art motion estimation
accuracy on the MPI Sintel dataset. Experiments on a custom dataset with
topological event annotations demonstrate the effectiveness of our pipeline in
estimating motion on event boundaries, as well as promising performance in
explicit topological event detection
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