52,308 research outputs found
Analysis of Robust Functions for Registration Algorithms
Registration accuracy is influenced by the presence of outliers and numerous
robust solutions have been developed over the years to mitigate their effect.
However, without a large scale comparison of solutions to filter outliers, it
is becoming tedious to select an appropriate algorithm for a given application.
This paper presents a comprehensive analyses of the effects of outlier filters
on the ICP algorithm aimed at mobile robotic application. Fourteen of the most
common outlier filters (such as M-estimators) have been tested in different
types of environments, for a total of more than two million registrations.
Furthermore, the influence of tuning parameters have been thoroughly explored.
The experimental results show that most outlier filters have similar
performance if they are correctly tuned. Nonetheless, filters such as Var.
Trim., Cauchy, and Cauchy MAD are more stable against different environment
types. Interestingly, the simple norm L1 produces comparable accuracy, while
been parameterless
Multi-scale Non-Rigid Point Cloud Registration Using Robust Sliced-Wasserstein Distance via Laplace-Beltrami Eigenmap
In this work, we propose computational models and algorithms for point cloud
registration with non-rigid transformation. First, point clouds sampled from
manifolds originally embedded in some Euclidean space are
transformed to new point clouds embedded in by
Laplace-Beltrami(LB) eigenmap using the leading eigenvalues and
corresponding eigenfunctions of LB operator defined intrinsically on the
manifolds. The LB eigenmap are invariant under isometric transformation of the
original manifolds. Then we design computational models and algorithms for
registration of the transformed point clouds in distribution/probability form
based on the optimal transport theory which provides both generality and
flexibility to handle general point clouds setting. Our methods use robust
sliced-Wasserstein distance, which is as the average of projected Wasserstein
distance along different directions, and incorporate a rigid transformation to
handle ambiguities introduced by the Laplace-Beltrami eigenmap. By going from
smaller , which provides a quick and robust registration (based on coarse
scale features) as well as a good initial guess for finer scale registration,
to a larger , our method provides an efficient, robust and accurate approach
for multi-scale non-rigid point cloud registration.Comment: 29 pages, 8 figure
Robust registration of medical images in the presence of spatially-varying noise
Spatially-varying intensity noise is a common source of distortion in medical
images. Bias field noise is one example of such a distortion that is often
present in the magnetic resonance (MR) images or other modalities such as
retina images. In this paper, we first show that the bias field noise can be
considerably reduced using Empirical Mode Decomposition (EMD) technique. EMD is
a multi-resolution tool that decomposes a signal into several principle
patterns and residual components. We show that the spatially-varying noise is
highly expressed in the residual component of the EMD and could be filtered
out. Then, we propose two hierarchical multi-resolution EMD-based algorithms
for robust registration of images in the presence of spatially varying noise.
One algorithm (LR-EMD) is based on registration of EMD feature-maps from both
floating and reference images in various resolution levels. In the second
algorithm (AFR-EMD), we first extract an average feature-map based on EMD from
both floating and reference images. Then, we use a simple hierarchical
multi-resolution algorithm to register the average feature-maps. For the brain
MR images, both algorithms achieve lower error rate and higher convergence
percentage compared to the intensity-based hierarchical registration.
Specifically, using mutual information as the similarity measure, AFR-EMD
achieves 42% lower error rate in intensity and 52% lower error rate in
transformation compared to intensity-based hierarchical registration. For
LR-EMD, the error rate is 32% lower for the intensity and 41% lower for the
transformation. Furthermore, we demonstrate that our proposed algorithms
improve the registration of retina images in the presence of spatially varying
noise
Dependent landmark drift: robust point set registration with a Gaussian mixture model and a statistical shape model
The goal of point set registration is to find point-by-point correspondences
between point sets, each of which characterizes the shape of an object. Because
local preservation of object geometry is assumed, prevalent algorithms in the
area can often elegantly solve the problems without using geometric information
specific to the objects. This means that registration performance can be
further improved by using prior knowledge of object geometry. In this paper, we
propose a novel point set registration method using the Gaussian mixture model
with prior shape information encoded as a statistical shape model. Our
transformation model is defined as a combination of the similar transformation,
motion coherence, and the statistical shape model. Therefore, the proposed
method works effectively if the target point set includes outliers and missing
regions, or if it is rotated. The computational cost can be reduced to linear,
and therefore the method is scalable to large point sets. The effectiveness of
the method will be verified through comparisons with existing algorithms using
datasets concerning human body shapes, hands, and faces
Robust Registration of Gaussian Mixtures for Colour Transfer
We present a flexible approach to colour transfer inspired by techniques
recently proposed for shape registration. Colour distributions of the palette
and target images are modelled with Gaussian Mixture Models (GMMs) that are
robustly registered to infer a non linear parametric transfer function. We show
experimentally that our approach compares well to current techniques both
quantitatively and qualitatively. Moreover, our technique is computationally
the fastest and can take efficient advantage of parallel processing
architectures for recolouring images and videos. Our transfer function is
parametric and hence can be stored in memory for later usage and also combined
with other computed transfer functions to create interesting visual effects.
Overall this paper provides a fast user friendly approach to recolouring of
image and video materials
Discriminative Optimization: Theory and Applications to Computer Vision Problems
Many computer vision problems are formulated as the optimization of a cost
function. This approach faces two main challenges: (i) designing a cost
function with a local optimum at an acceptable solution, and (ii) developing an
efficient numerical method to search for one (or multiple) of these local
optima. While designing such functions is feasible in the noiseless case, the
stability and location of local optima are mostly unknown under noise,
occlusion, or missing data. In practice, this can result in undesirable local
optima or not having a local optimum in the expected place. On the other hand,
numerical optimization algorithms in high-dimensional spaces are typically
local and often rely on expensive first or second order information to guide
the search. To overcome these limitations, this paper proposes Discriminative
Optimization (DO), a method that learns search directions from data without the
need of a cost function. Specifically, DO explicitly learns a sequence of
updates in the search space that leads to stationary points that correspond to
desired solutions. We provide a formal analysis of DO and illustrate its
benefits in the problem of 3D point cloud registration, camera pose estimation,
and image denoising. We show that DO performed comparably or outperformed
state-of-the-art algorithms in terms of accuracy, robustness to perturbations,
and computational efficiency.Comment: 26 pages, 28 figure
Outlier-Robust Spatial Perception: Hardness, General-Purpose Algorithms, and Guarantees
Spatial perception is the backbone of many robotics applications, and spans a
broad range of research problems, including localization and mapping, point
cloud alignment, and relative pose estimation from camera images. Robust
spatial perception is jeopardized by the presence of incorrect data
association, and in general, outliers. Although techniques to handle outliers
do exist, they can fail in unpredictable manners (e.g., RANSAC, robust
estimators), or can have exponential runtime (e.g., branch-and-bound). In this
paper, we advance the state of the art in outlier rejection by making three
contributions. First, we show that even a simple linear instance of outlier
rejection is inapproximable: in the worst-case one cannot design a
quasi-polynomial time algorithm that computes an approximate solution
efficiently. Our second contribution is to provide the first per-instance
sub-optimality bounds to assess the approximation quality of a given outlier
rejection outcome. Our third contribution is to propose a simple
general-purpose algorithm, named adaptive trimming, to remove outliers. Our
algorithm leverages recently-proposed global solvers that are able to solve
outlier-free problems, and iteratively removes measurements with large errors.
We demonstrate the proposed algorithm on three spatial perception problems: 3D
registration, two-view geometry, and SLAM. The results show that our algorithm
outperforms several state-of-the-art methods across applications while being a
general-purpose method
Image Registration of Very Large Images via Genetic Programming
Image registration (IR) is a fundamental task in image processing for
matching two or more images of the same scene taken at different times, from
different viewpoints and/or by different sensors. Due to the enormous diversity
of IR applications, automatic IR remains a challenging problem to this day. A
wide range of techniques has been developed for various data types and
problems. However, they might not handle effectively very large images, which
give rise usually to more complex transformations, e.g., deformations and
various other distortions.
In this paper we present a genetic programming (GP)-based approach for IR,
which could offer a significant advantage in dealing with very large images, as
it does not make any prior assumptions about the transformation model. Thus, by
incorporating certain generic building blocks into the proposed GP framework,
we hope to realize a large set of specialized transformations that should yield
accurate registration of very large images
Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models
Matching 3D rigid point clouds in complex environments robustly and
accurately is still a core technique used in many applications. This paper
proposes a new architecture combining error estimation from sample covariances
and dual global probability alignment based on the convolution of adaptive
Gaussian Mixture Models (GMM) from point clouds. Firstly, a novel adaptive GMM
is defined using probability distributions from the corresponding points. Then
rigid point cloud alignment is performed by maximizing the global probability
from the convolution of dual adaptive GMMs in the whole 2D or 3D space, which
can be efficiently optimized and has a large zone of accurate convergence.
Thousands of trials have been conducted on 200 models from public 2D and 3D
datasets to demonstrate superior robustness and accuracy in complex
environments with unpredictable noise, outliers, occlusion, initial rotation,
shape and missing points.Comment: 9 page
Image Registration Techniques: A Survey
Image Registration is the process of aligning two or more images of the same
scene with reference to a particular image. The images are captured from
various sensors at different times and at multiple view-points. Thus to get a
better picture of any change of a scene or object over a considerable period of
time image registration is important. Image registration finds application in
medical sciences, remote sensing and in computer vision. This paper presents a
detailed review of several approaches which are classified accordingly along
with their contributions and drawbacks. The main steps of an image registration
procedure are also discussed. Different performance measures are presented that
determine the registration quality and accuracy. The scope for the future
research are presented as well
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